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Pitakaso R, Srichok T, Khonjun S, Golinska-Dawson P, Gonwirat S, Nanthasamroeng N, Boonmee C, Jirasirilerd G, Luesak P. Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification. WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 183:87-100. [PMID: 38735094 DOI: 10.1016/j.wasman.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/26/2024] [Accepted: 05/04/2024] [Indexed: 05/14/2024]
Abstract
This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices.
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Affiliation(s)
- Rapeepan Pitakaso
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
| | - Thanatkij Srichok
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
| | - Surajet Khonjun
- Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand.
| | - Paulina Golinska-Dawson
- Institute of Logistics, Poznan University of Technology, Jacka Rychlewskiego 2 Street, 60-965 Poznan, Poland.
| | - Sarayut Gonwirat
- Department of Computer Engineering and Automation Kalasin University, Kalasin 46000, Thailand.
| | - Natthapong Nanthasamroeng
- Artificial Intelligence Optimization SMART Laboratory, Engineering Technology Department, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand.
| | - Chawis Boonmee
- Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
| | - Ganokgarn Jirasirilerd
- Department of Industrial and Environmental Management Engineering, Faculty of Liberal Arts and Sciences, Sisaket Rajabhat University, Sisaket 33000, Thailand.
| | - Peerawat Luesak
- Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai 57120, Thailand.
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Gore S, Dhole A, Kumbhar S, Jagtap J. Radiomics for Parkinson's disease classification using advanced texture-based biomarkers. MethodsX 2023; 11:102359. [PMID: 37791007 PMCID: PMC10543659 DOI: 10.1016/j.mex.2023.102359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP.•Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder.•Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis.•The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection.
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Affiliation(s)
- Sonal Gore
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Aniket Dhole
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Shrishail Kumbhar
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Jayant Jagtap
- Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), (SIU), Lavale, Pune, Maharashtra, India
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Amin E, Elgammal YM, Zahran MA, Abdelsalam MM. Alzheimer's disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm. Sci Rep 2023; 13:18568. [PMID: 37903890 PMCID: PMC10616199 DOI: 10.1038/s41598-023-45972-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 10/26/2023] [Indexed: 11/01/2023] Open
Abstract
Alzheimer's disease (AD) is a physical illness, which damages a person's brain; it is the most common cause of dementia. AD can be characterized by the formation of amyloid-beta (Aβ) deposits. They exhibit diverse morphologies that range from diffuse to dense-core plaques. Most of the histological images cannot be described precisely by traditional geometry or methods. Therefore, this study aims to employ multifractal geometry in assessing and classifying amyloid plaque morphologies. The classification process is based on extracting the most descriptive features related to the amyloid-beta (Aβ) deposits using the Naive Bayes classifier. To eliminate the less important features, the Random Forest algorithm has been used. The proposed methodology has achieved an accuracy of 99%, sensitivity of 100%, and specificity of 98.5%. This study employed a new dataset that had not been widely used before.
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Affiliation(s)
- Elshaimaa Amin
- Future Higher Institute of Engineering and Technology, Mansoura, Egypt
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Yasmina M Elgammal
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - M A Zahran
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Mohamed M Abdelsalam
- Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
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Sharma R, Goel T, Tanveer M, Suganthan PN, Razzak I, Murugan R. Conv-eRVFL: Convolutional Neural Network Based Ensemble RVFL Classifier for Alzheimer's Disease Diagnosis. IEEE J Biomed Health Inform 2023; 27:4995-5003. [PMID: 36260567 DOI: 10.1109/jbhi.2022.3215533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
As per the latest statistics, Alzheimer's disease (AD) has become a global burden over the following decades. Identifying AD at the intermediate stage became challenging, with mild cognitive impairment (MCI) utilizing credible biomarkers and robust learning approaches. Neuroimaging techniques like magnetic resonance imaging (MRI) and positron emission tomography (PET) are practical research approaches that provide structural atrophies and metabolic variations. With the help of MRI and PET scans, metabolic and structural changes in AD patients can be visible even ten years before the disease's onset. This paper proposes a novel wavelet packet transform-based structural and metabolic image fusion approach using MRI and PET scans. An eight-layer trained CNN extracts features from multiple layers and these features are fed to an ensemble of non-iterative random vector functional link (RVFL) models. The RVFL network incorporates the s-membership fuzzy function as an activation function that helps overcome outliers. Lastly, outputs of all the customized RVFL classifiers are averaged and fed to the RVFL classifier to make the final decision. Experiments are performed over Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and classification is made over CN vs. AD vs. MCI. The model performance obtained is decent enough to prove the effectiveness of the fusion-based ensemble approach.
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Tang C, Wei M, Sun J, Wang S, Zhang Y. CsAGP: Detecting Alzheimer's disease from multimodal images via dual-transformer with cross-attention and graph pooling. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101618. [PMID: 38559705 PMCID: PMC7615783 DOI: 10.1016/j.jksuci.2023.101618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease (AD) is a terrible and degenerative disease commonly occurring in the elderly. Early detection can prevent patients from further damage, which is crucial in treating AD. Over the past few decades, it has been demonstrated that neuroimaging can be a critical diagnostic tool for AD, and the feature fusion of different neuroimaging modalities can enhance diagnostic performance. Most previous studies in multimodal feature fusion have only concatenated the high-level features extracted by neural networks from various neuroimaging images simply. However, a major problem of these studies is over-looking the low-level feature interactions between modalities in the feature extraction stage, resulting in suboptimal performance in AD diagnosis. In this paper, we develop a dual-branch vision transformer with cross-attention and graph pooling, namely CsAGP, which enables multi-level feature interactions between the inputs to learn a shared feature representation. Specifically, we first construct a brand-new cross-attention fusion module (CAFM), which processes MRI and PET images by two independent branches of differing computational complexity. These features are fused merely by the cross-attention mechanism to enhance each other. After that, a concise graph pooling algorithm-based Reshape-Pooling-Reshape (RPR) framework is developed for token selection to reduce token redundancy in the proposed model. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the suggested method obtains 99.04%, 97.43%, 98.57%, and 98.72% accuracy for the classification of AD vs. CN, AD vs. MCI, CN vs. MCI, and AD vs. CN vs. MCI, respectively.
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Affiliation(s)
- Chaosheng Tang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
| | - Mingyang Wei
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
| | - Shuihua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Yudong Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, PR China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Miller WM, Ziegler KM, Yilmaz A, Saiyed N, Ustun I, Akyol S, Idler J, Sims MD, Maddens ME, Graham SF. Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes. Metabolites 2023; 13:metabo13040506. [PMID: 37110164 PMCID: PMC10145663 DOI: 10.3390/metabo13040506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.
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Affiliation(s)
- Wendy M. Miller
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Oakland University William Beaumont School of Medicine, 586 Pioneer Dr, Rochester, MI 48309, USA
| | - Kathryn M. Ziegler
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Oakland University William Beaumont School of Medicine, 586 Pioneer Dr, Rochester, MI 48309, USA
| | - Ali Yilmaz
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
| | - Nazia Saiyed
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
| | - Ilyas Ustun
- DePaul University Jarvis College of Computing and Digital Media, 243 S Wabash Ave, Chicago, IL 60604, USA
| | - Sumeyya Akyol
- NX Prenatal Inc. Laboratory, 4800 Fournace Place, Suite BW28, Bellaire, TX 77401, USA
| | - Jay Idler
- Allegheny Health Network, West Penn Hospital, 4815 Liberty Ave, Suite GR50, Pittsburgh, PA 15224, USA
- Drexel University College of Medicine, 2900 W Queen Ln, Philadelphia, PA 19129, USA
| | - Matthew D. Sims
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
| | - Michael E. Maddens
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Oakland University William Beaumont School of Medicine, 586 Pioneer Dr, Rochester, MI 48309, USA
| | - Stewart F. Graham
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
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El-Sappagh S, Alonso-Moral JM, Abuhmed T, Ali F, Bugarín-Diz A. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10415-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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8
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Alam Suha S, Islam MN. Exploring the Dominant Features and Data-driven Detection of Polycystic Ovary Syndrome through Modified Stacking Ensemble Machine Learning Technique. Heliyon 2023; 9:e14518. [PMID: 36994397 PMCID: PMC10040521 DOI: 10.1016/j.heliyon.2023.e14518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/09/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with 'Gradient Boosting' classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.
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Marjit S, Bhattacharyya T, Chatterjee B, Sarkar R. Simulated annealing aided genetic algorithm for gene selection from microarray data. Comput Biol Med 2023; 158:106854. [PMID: 37023541 DOI: 10.1016/j.compbiomed.2023.106854] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/26/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic Algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic Algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://github.com/shyammarjit/SAGA.
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Hazarika BB, Gupta D, Kumar B. EEG Signal Classification Using a Novel Universum-Based Twin Parametric-Margin Support Vector Machine. Cognit Comput 2023. [DOI: 10.1007/s12559-023-10115-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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11
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Zhou K, Piao S, Liu X, Luo X, Chen H, Xiang R, Geng D. A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction. Front Aging Neurosci 2023; 14:1073909. [PMID: 36726800 PMCID: PMC9884698 DOI: 10.3389/fnagi.2022.1073909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning. Methods In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve. Results Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification. Discussion The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.
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Affiliation(s)
- Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Rui Xiang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China,*Correspondence: Daoying Geng,
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Moosaei H, Ganaie M, Hladík M, Tanveer M. Inverse free reduced universum twin support vector machine for imbalanced data classification. Neural Netw 2023; 157:125-135. [DOI: 10.1016/j.neunet.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 11/09/2022]
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Elgammal YM, Zahran MA, Abdelsalam MM. A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm. Sci Rep 2022; 12:22381. [PMID: 36572791 PMCID: PMC9792538 DOI: 10.1038/s41598-022-26958-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 12/22/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's Disease (AD) is considered one of the most diseases that much prevalent among elderly people all over the world. AD is an incurable neurodegenerative disease affecting cognitive functions and were characterized by progressive and collective functions deteriorating. Remarkably, early detection of AD is essential for the development of new and invented treatment strategies. As Dementia causes irreversible damage to the brain neurons and leads to changes in its structure that can be described adequately within the framework of multifractals. Hence, the present work focus on developing a promising and efficient computing technique to pre-process and classify the AD disease especially in the early stages using multifractal geometry to extract the most changeable features due to AD. Then, A machine learning classification algorithm (K-Nearest Neighbor) has been implemented in order to classify and detect the main four early stages of AD. Two datasets have been used to ensure the validation of the proposed methodology. The proposed technique has achieved 99.4% accuracy and 100% sensitivity. The comparative results show that the proposed classification technique outperforms is recent techniques in terms of performance measures.
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Affiliation(s)
- Yasmina M Elgammal
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - M A Zahran
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Mohamed M Abdelsalam
- Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
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Pan X, Zhang G, Lin A, Guan X, Chen P, Ge Y, Chen X. An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm. Comput Biol Med 2022; 151:106229. [PMID: 36308897 DOI: 10.1016/j.compbiomed.2022.106229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/08/2022] [Accepted: 10/16/2022] [Indexed: 12/27/2022]
Abstract
Foot & ankle deformity is a chronic disease with high incidence and is best treated in childhood. However, the current diagnostic procedures rely on doctor's consultation and empirical judgment, and lack objective and quantitative evaluation methods, resulting in low screening rates. To solve this problem, this paper aims to construct an evaluation model for children's foot & ankle deformity through data mining and machine learning technologies. Firstly, it proposes the grading rules for children's foot & ankle deformity severity based on analyzing the existing quantitative indexes and expert experience. Then the 3D foot scanner is used to collect the sample data including 30 foot structure indexes. Finally, an advanced sparse multi-objective evolutionary algorithm (sparse MO-FS) is present for feature selection. The effectiveness of the proposed sparse MO-FS and its search efficiency are proved by comparing 8 feature selection methods and 7 search strategies. Using sparse MO-FS, foot length, arch index, ankle index, and hallux valgus index are selected, which not only simplifies the evaluation model but also improves the average classification accuracy of random forest to more than 98%.
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Affiliation(s)
- Xiaotian Pan
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China.
| | - Guodao Zhang
- School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Aiju Lin
- College of international Education, Wenzhou University, Wenzhou 325035, China.
| | - Xiaochun Guan
- Department of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
| | - PingKuo Chen
- Great Bay University, Dongguan City 523000, China.
| | - Yisu Ge
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325100, China.
| | - Xin Chen
- Orthopedics Department of The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
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15
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Li WK, Chen YC, Xu XW, Wang X, Gao X. Human-Guided Functional Connectivity Network Estimation for Chronic Tinnitus Identification: A Modularity View. IEEE J Biomed Health Inform 2022; 26:4849-4858. [PMID: 35830394 DOI: 10.1109/jbhi.2022.3190277] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The functional connectivity network (FCN) has been used to achieve several remarkable advancements in the diagnosis of neuro-degenerative disorders. Therefore, it is imperative to accurately estimate biologically meaningful FCNs. Several efforts have been dedicated to this purpose by encoding biological priors. However, owing to the high complexity of the human brain, the estimation of an 'ideal' FCN remains an open problem. To the best of our knowledge, almost all existing studies lack the integration of domain expert knowledge, which limits their performance. In this study, we focused on incorporating domain expert knowledge into the FCN estimation from a modularity perspective. To achieve this, we presented a human-guided modular representation (MR) FCN estimation framework. Specifically, we designed an adversarial low-rank constraint to describe the module structure of FCNs under the guidance of domain expert knowledge (i.e., a predefined participant index). The chronic tinnitus (TIN) identification task based on the estimated FCNs was conducted to examine the proposed MR methods. Remarkably, MR significantly outperformed the baseline and state-of-the-art(SOTA) methods, achieving an accuracy of 92.11%. Moreover, post-hoc analysis revealed that the FCNs estimated by the proposed MR could highlight more biologically meaningful connections, which is beneficial for exploring the underlying mechanisms of TIN and diagnosing early TIN.
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Perpetuini D, Filippini C, Zito M, Cardone D, Merla A. Altered Microcirculation in Alzheimer’s Disease Assessed by Machine Learning Applied to Functional Thermal Imaging Data. Bioengineering (Basel) 2022; 9:bioengineering9100492. [PMID: 36290459 PMCID: PMC9598647 DOI: 10.3390/bioengineering9100492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/24/2022] Open
Abstract
Alzheimer’s disease (AD) is characterized by progressive memory failures accompanied by microcirculation alterations. Particularly, impaired endothelial microvascular responsiveness and altered flow motion patterns have been observed in AD patients. Of note, the endothelium influences the vascular tone and also the small superficial blood vessels, which can be evaluated through infrared thermography (IRT). The advantage of IRT with respect to other techniques relies on its contactless features and its capability to preserve spatial information of the peripheral microcirculation. The aim of the study is to investigate peripheral microcirculation impairments in AD patients with respect to age-matched healthy controls (HCs) at resting state, through IRT and machine learning (ML) approaches. Particularly, several classifiers were tested, employing as regressors the power of the nose tip temperature time course in different physiological frequency bands. Among the ML classifiers tested, the Decision Tree Classifier (DTC) delivered the best cross-validated accuracy (accuracy = 82%) when discriminating between AD and HCs. The results further demonstrate the alteration of microvascular patterns in AD in the early stages of the pathology, and the capability of IRT to assess vascular impairments. These findings could be exploited in clinical practice, fostering the employment of IRT as a support for the early diagnosis of AD.
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Affiliation(s)
- David Perpetuini
- Department of Neuroscience and Imaging, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
- Correspondence: ; Tel.: +39-0871-3556954
| | - Chiara Filippini
- Department of Neuroscience and Imaging, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Michele Zito
- Department of Medicine and Science of Ageing, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Daniela Cardone
- Department of Engineering and Geology, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy
- Next2U s.r.l., 65127 Pescara, Italy
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Abdelwahab O, Awad N, Elserafy M, Badr E. A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma. PLoS One 2022; 17:e0269126. [PMID: 36067196 PMCID: PMC9447897 DOI: 10.1371/journal.pone.0269126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 05/15/2022] [Indexed: 12/23/2022] Open
Abstract
Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random Forest (RF) as an embedded FS technique. The results were integrated and candidate biomarker genes across all techniques were identified. The proposed framework has identified 12 potential biomarkers that are highly correlated with different LC types, especially LUAD. A predictive model has been trained utilizing the identified biomarker expression profiling and performance of 97.99% was achieved. In addition, upon performing differential gene expression analysis, we could find that all 12 genes were significantly differentially expressed between normal and LUAD tissues, and strongly correlated with LUAD according to previous reports. We here propose that using multiple feature selection methods effectively reduces the number of identified biomarkers and directly affects their biological relevance.
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Affiliation(s)
- Omar Abdelwahab
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
| | - Nourelislam Awad
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
- Center of Informatics Science, Nile university, Giza, Egypt
| | - Menattallah Elserafy
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
| | - Eman Badr
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
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Tu Y, Lin S, Qiao J, Zhuang Y, Zhang P. Alzheimer's disease diagnosis via multimodal feature fusion. Comput Biol Med 2022; 148:105901. [PMID: 35908497 DOI: 10.1016/j.compbiomed.2022.105901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/26/2022] [Accepted: 07/16/2022] [Indexed: 11/19/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly. Early diagnosis of AD plays a vital role in slowing down the progress of AD because there is no effective drug to treat the disease. Some deep learning models have recently been presented for AD diagnosis and have more satisfactory performance than classic machine learning methods. Nevertheless, most of the existing computer-aided diagnostic models used neuroimaging features for diagnosis, ignoring patients' clinical and biological information. This makes the AD diagnosis inaccurate. In this study, we propose a novel multimodal feature transformation and fusion model for AD diagnosis. The feature transformation aims to avoid the difference in feature dimensions between different modal data and further mine the significant features for AD diagnosis. A geometric algebra-based feature extension method is proposed to obtain different levels of high-dimensional features from patients' clinical and personal biological data. Then, an influence degree-based feature filtration algorithm is proposed to filtrate those features that have no apparent guiding significance for AD diagnosis. Finally, an ANN (Artificial Neural Network)-based framework is designed to fuse transformed features with neuroimaging features extracted by CNN (Convolutional Neural Network) for AD diagnosis. The more in-depth feature mining of patients' clinical information and biological information can significantly improve the performance of computer-aided AD diagnosis. The experiments are obtained on the ADNI dataset. Our proposed model can converge faster and achieves 96.2% accuracy in AD diagnostic task and 87.4% accuracy in MCI (Mild Cognitive Impairment) diagnostic task. Compared with other methods, our proposed approach has an excellent performance in AD diagnosis and surpasses SOTA (state-of-the-art) methods. Therefore, our model can provide more reasonable suggestions for clinicians to diagnose and treat disease.
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Affiliation(s)
- Yue Tu
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Shukuan Lin
- School of Computer Science and Engineering, Northeastern University, Shenyang, China.
| | - Jianzhong Qiao
- School of Computer Science and Engineering, Northeastern University, Shenyang, China.
| | - Yilin Zhuang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Peng Zhang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
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19
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Ganaie M, Tanveer M. KNN weighted reduced universum twin SVM for class imbalance learning. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108578] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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20
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Lu J, Zeng W, Zhang L, Shi Y. A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study. Front Aging Neurosci 2022; 14:888575. [PMID: 35693342 PMCID: PMC9177228 DOI: 10.3389/fnagi.2022.888575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/25/2022] [Indexed: 12/31/2022] Open
Abstract
The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer’s disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy.
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Affiliation(s)
- Jia Lu
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China
- *Correspondence: Weiming Zeng,
| | - Lu Zhang
- Basic Experiment and Training Center, Shanghai Maritime University, Shanghai, China
| | - Yuhu Shi
- College of Information Engineering Shanghai Maritime University, Shanghai, China
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21
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A CAD system design for Alzheimer's disease diagnosis using temporally consistent clustering and hybrid deep learning models. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive. ENTROPY 2022; 24:e24040471. [PMID: 35455133 PMCID: PMC9025839 DOI: 10.3390/e24040471] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 12/04/2022]
Abstract
(1) Background and objective: Cardiovascular disease is one of the most common causes of death in today’s world. ECG is crucial in the early detection and prevention of cardiovascular disease. In this study, an improved deep learning method is proposed to diagnose abnormal and normal ECG accurately. (2) Methods: This paper proposes a CNN-FWS that combines three convolutional neural networks (CNN) and recursive feature elimination based on feature weights (FW-RFE), which diagnoses abnormal and normal ECG. F1 score and Recall are used to evaluate the performance. (3) Results: A total of 17,259 records were used in this study, which validated the diagnostic performance of CNN-FWS for normal and abnormal ECG signals in 12 leads. The experimental results show that the F1 score of CNN-FWS is 0.902, and the Recall of CNN-FWS is 0.889. (4) Conclusion: CNN-FWS absorbs the advantages of convolutional neural networks (CNN) to obtain three parts of different spatial information and enrich the learned features. CNN-FWS can select the most relevant features while eliminating unrelated and redundant features by FW-RFE, making the residual features more representative and effective. The method is an end-to-end modeling approach that enables an adaptive feature selection process without human intervention.
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24
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Belei C, Joeressen J, Amancio-Filho ST. Fused-Filament Fabrication of Short Carbon Fiber-Reinforced Polyamide: Parameter Optimization for Improved Performance under Uniaxial Tensile Loading. Polymers (Basel) 2022; 14:polym14071292. [PMID: 35406166 PMCID: PMC9002508 DOI: 10.3390/polym14071292] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023] Open
Abstract
This study intends to contribute to the state of the art of Fused-Filament Fabrication (FFF) of short-fiber-reinforced polyamides by optimizing process parameters to improve the performance of printed parts under uniaxial tensile loading. This was performed using two different approaches: a more traditional 2k full factorial design of experiments (DoE) and multiple polynomial regression using an algorithm implementing machine learning (ML) principles such as train-test split and cross-validation. Evaluated parameters included extrusion and printing bed temperatures, layer height and printing speed. It was concluded that when exposed to new observations, the ML-based model predicted the response with higher accuracy. However, the DoE fared slightly better at predicting observations where higher response values were expected, including the optimal solution, which reached an UTS of 117.1 ± 5.7 MPa. Moreover, there was an important correlation between process parameters and the response. Layer height and printing bed temperatures were considered the most influential parameters, while extrusion temperature and printing speed had a lower influence on the outcome. The general influence of parameters on the response was correlated with the degree of interlayer cohesion, which in turn affected the mechanical performance of the 3D-printed specimens.
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25
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Machine Learning Framework for the Prediction of Alzheimer’s Disease Using Gene Expression Data Based on Efficient Gene Selection. Symmetry (Basel) 2022. [DOI: 10.3390/sym14030491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
In recent years, much research has focused on using machine learning (ML) for disease prediction based on gene expression (GE) data. However, many diseases have received considerable attention, whereas some, including Alzheimer’s disease (AD), have not, perhaps due to data shortage. The present work is intended to fill this gap by introducing a symmetric framework to predict AD from GE data, with the aim to produce the most accurate prediction using the smallest number of genes. The framework works in four stages after it receives a training dataset: pre-processing, gene selection (GS), classification, and AD prediction. The symmetry of the model is manifested in all of its stages. In the pre-processing stage gene columns in the training dataset are pre-processed identically. In the GS stage, the same user-defined filter metrics are invoked on every gene individually, and so are the same user-defined wrapper metrics. In the classification stage, a number of user-defined ML models are applied identically using the minimal set of genes selected in the preceding stage. The core of the proposed framework is a meticulous GS algorithm which we have designed to nominate eight subsets of the original set of genes provided in the training dataset. Exploring the eight subsets, the algorithm selects the best one to describe AD, and also the best ML model to predict the disease using this subset. For credible results, the framework calculates performance metrics using repeated stratified k-fold cross validation. To evaluate the framework, we used an AD dataset of 1157 cases and 39,280 genes, obtained by combining a number of smaller public datasets. The cases were split in two partitions, 1000 for training/testing, using 10-fold CV repeated 30 times, and 157 for validation. From the testing/training phase, the framework identified only 1058 genes to be the most relevant and the support vector machine (SVM) model to be the most accurate with these genes. In the final validation, we used the 157 cases that were never seen by the SVM classifier. For credible performance evaluation, we evaluated the classifier via six metrics, for which we obtained impressive values. Specifically, we obtained 0.97, 0.97, 0.98, 0.945, 0.972, and 0.975 for the sensitivity (recall), specificity, precision, kappa index, AUC, and accuracy, respectively.
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Jiang C, Jiang W. AGTR1, PLTP, and SCG2 associated with immune genes and immune cell infiltration in calcific aortic valve stenosis: analysis from integrated bioinformatics and machine learning. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3787-3802. [PMID: 35341274 DOI: 10.3934/mbe.2022174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Background: Calcific aortic valve stenosis (CAVS) is a crucial cardiovascular disease facing aging societies. Our research attempts to identify immune-related genes through bioinformatics and machine learning analysis. Two machine learning strategies include Least Absolute Shrinkage Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). In addition, we deeply explore the role of immune cell infiltration in CAVS, aiming to study the potential therapeutic targets of CAVS and explore possible drugs. Methods: Download three data sets related to CAVS from the Gene Expression Omnibus. Gene set variation analysis (GSVA) looks for potential mechanisms, determines differentially expressed immune-related genes (DEIRGs) by combining the ImmPort database with CAVS differential genes, and explores the functions and pathways of enrichment. Two machine learning methods, LASSO and SVM-RFE, screen key immune signals and validate them in external data sets. Single-sample GSEA (ssGSEA) and CIBERSORT analyze the subtypes of immune infiltrating cells and integrate the analysis with DEIRGs and key immune signals. Finally, the possible targeted drugs are analyzed through the Connectivity Map (CMap). Results: GSVA analysis of the gene set suggests that it is highly correlated with multiple immune pathways. 266 differential genes (DEGs) integrate with immune genes to obtain 71 DEIRGs. Enrichment analysis found that DEIRGs are related to oxidative stress, synaptic membrane components, receptor activity, and a variety of cardiovascular diseases and immune pathways. Angiotensin II Receptor Type 1(AGTR1), Phospholipid Transfer Protein (PLTP), Secretogranin II (SCG2) are identified as key immune signals of CAVS by machine learning. Immune infiltration found that B cells naï ve and Macrophages M2 are less in CAVS, while Macrophages M0 is more in CAVS. Simultaneously, AGTR1, PLTP, SCG2 are highly correlated with a variety of immune cell subtypes. CMap analysis found that isoliquiritigenin, parthenolide, and pyrrolidine-dithiocarbamate are the top three targeted drugs related to CAVS immunity. Conclusion: The key immune signals, immune infiltration and potential drugs obtained from the research play a vital role in the pathophysiological progress of CAVS.
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Affiliation(s)
- Chenyang Jiang
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Weidong Jiang
- Department of Cardiology, Nantong Traditional Chinese Medicine Hospital, Nantong 226001, China
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27
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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28
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Sharma R, Goel T, Tanveer M, Murugan R. FDN-ADNet: Fuzzy LS-TWSVM based deep learning network for prognosis of the Alzheimer’s disease using the sagittal plane of MRI scans. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108099] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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29
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Evaluation of Feature Selection Methods on Psychosocial Education Data Using Additive Ratio Assessment. ELECTRONICS 2021. [DOI: 10.3390/electronics11010114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Artificial intelligence, particularly machine learning, is the fastest-growing research trend in educational fields. Machine learning shows an impressive performance in many prediction models, including psychosocial education. The capability of machine learning to discover hidden patterns in large datasets encourages researchers to invent data with high-dimensional features. In contrast, not all features are needed by machine learning, and in many cases, high-dimensional features decrease the performance of machine learning. The feature selection method is one of the appropriate approaches to reducing the features to ensure machine learning works efficiently. Various selection methods have been proposed, but research to determine the essential subset feature in psychosocial education has not been established thus far. This research investigated and proposed methods to determine the best feature selection method in the domain of psychosocial education. We used a multi-criteria decision system (MCDM) approach with Additive Ratio Assessment (ARAS) to rank seven feature selection methods. The proposed model evaluated the best feature selection method using nine criteria from the performance metrics provided by machine learning. The experimental results showed that the ARAS is promising for evaluating and recommending the best feature selection method for psychosocial education data using the teacher’s psychosocial risk levels dataset.
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30
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Chen X, Jiang Z. ISFMDA: Learning Interactions of Selected Features-Based Method for Predicting Potential MicroRNA-Disease Associations. J Comput Biol 2021; 28:1219-1227. [PMID: 34847740 DOI: 10.1089/cmb.2021.0149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Prediction of potential microRNA-disease associations is one of the important tasks in computational biology fields. Mining more sophisticated features can improve the performance of the prediction methods. This article proposes a novel algorithm (ISFMDA) that can effectively learn low- or high-order interactions of recursive feature elimination selected features by an extreme gradient boosting, a factorization machine, and a deep neural network. As a result, ISFMDA can obtain an area under receiver operating characteristic curve (AUROC) of 0.9342 ± 0.0007 in fivefold cross-validation tests with 51.25% of original features, which verifies the effectiveness of the methods.
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Affiliation(s)
- Xuejun Chen
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Zhenran Jiang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
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31
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Ganaie M, Tanveer M. Fuzzy least squares projection twin support vector machines for class imbalance learning. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107933] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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32
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Liu W, Wang J. Recursive elimination–election algorithms for wrapper feature selection. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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33
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Battineni G, Hossain MA, Chintalapudi N, Traini E, Dhulipalla VR, Ramasamy M, Amenta F. Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms. Diagnostics (Basel) 2021; 11:diagnostics11112103. [PMID: 34829450 PMCID: PMC8623867 DOI: 10.3390/diagnostics11112103] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 11/16/2022] Open
Abstract
Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer's disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.
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Affiliation(s)
- Gopi Battineni
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
- Correspondence: ; Tel.: +39-3331728206
| | - Mohmmad Amran Hossain
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
| | - Nalini Chintalapudi
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
| | - Enea Traini
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
| | - Venkata Rao Dhulipalla
- The Research Centre of the ECE Department, V.R. Siddhartha Engineering College, Vijayawada 521002, Andhra Pradesh, India; (V.R.D.); (M.R.)
| | - Mariappan Ramasamy
- The Research Centre of the ECE Department, V.R. Siddhartha Engineering College, Vijayawada 521002, Andhra Pradesh, India; (V.R.D.); (M.R.)
| | - Francesco Amenta
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy; (M.A.H.); (N.C.); (E.T.); (F.A.)
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Aurelia J, Rustam Z. A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer. Open Access Maced J Med Sci 2021. [DOI: 10.3889/oamjms.2021.6955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory.
AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM.
METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer.
RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value.
CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.
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Karami G, Giuseppe Orlando M, Delli Pizzi A, Caulo M, Del Gratta C. Predicting Overall Survival Time in Glioblastoma Patients Using Gradient Boosting Machines Algorithm and Recursive Feature Elimination Technique. Cancers (Basel) 2021; 13:4976. [PMID: 34638460 PMCID: PMC8507924 DOI: 10.3390/cancers13194976] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/20/2021] [Accepted: 09/29/2021] [Indexed: 11/16/2022] Open
Abstract
Despite advances in tumor treatment, the inconsistent response is a major challenge among glioblastoma multiform (GBM) that lead to different survival time. Our aim was to integrate multimodal MRI with non-supervised and supervised machine learning methods to predict GBM patients' survival time. To this end, we identified different compartments of the tumor and extracted their features. Next, we applied Random Forest-Recursive Feature Elimination (RF-RFE) to identify the most relevant features to feed into a GBoost machine. This study included 29 GBM patients with known survival time. RF-RFE GBoost model was evaluated to assess the survival prediction performance using optimal features. Furthermore, overall survival (OS) was analyzed using univariate and multivariate Cox regression analyses, to evaluate the effect of ROIs and their features on survival. The results showed that a RF-RFE Gboost machine was able to predict survival time with 75% accuracy. The results also revealed that the rCBV in the low perfusion area was significantly different between groups and had the greatest effect size in terms of the rate of change of the response variable (survival time). In conclusion, not only integration of multi-modality MRI but also feature selection method can enhance the classifier performance.
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Affiliation(s)
- Golestan Karami
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Marco Giuseppe Orlando
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Andrea Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Cosimo Del Gratta
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
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Sharma R, Goel T, Tanveer M, Dwivedi S, Murugan R. FAF-DRVFL: Fuzzy activation function based deep random vector functional links network for early diagnosis of Alzheimer disease. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107371] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method. REMOTE SENSING 2021. [DOI: 10.3390/rs13122273] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
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Tanveer M, Rashid AH, Ganaie MA, Reza M, Razzak I, Hua KL. Classification of Alzheimer's disease using ensemble of deep neural networks trained through transfer learning. IEEE J Biomed Health Inform 2021; 26:1453-1463. [PMID: 34033550 DOI: 10.1109/jbhi.2021.3083274] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Deep learning; transfer learning; ensemble learning; Alzheimer's disease.
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Tunç T, Alım Z. Synthesis of New Schiff Bases and Assessment of Their in vitro Biological Effects on Acetylcholinesterase and Carbonic Anhydrase Isoenzymes Activities. RUSSIAN JOURNAL OF ORGANIC CHEMISTRY 2021. [DOI: 10.1134/s1070428021020160] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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Moosaei H, Ketabchi S, Razzaghi M, Tanveer M. Generalized Twin Support Vector Machines. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10464-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Rathore H, Mohamed A, Guizani M, Rathore S. Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05704-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Agarwal M, Saba L, Gupta SK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Sharma AM, Viswanathan V, Kitas GD, Nicolaides A, Suri JS. Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application. Med Biol Eng Comput 2021; 59:511-533. [PMID: 33547549 DOI: 10.1007/s11517-021-02322-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/18/2021] [Indexed: 01/16/2023]
Abstract
Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.
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Affiliation(s)
- Mohit Agarwal
- CSE Department, Bennett University, Greater Noida, UP, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Suneet K Gupta
- CSE Department, Bennett University, Greater Noida, UP, India
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Ontario, Kingston, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Providence, RI, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention, National and Kapodistrian Univ. of Athens, Athens, Greece
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05596-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Yilmaz A, Ustun I, Ugur Z, Akyol S, Hu WT, Fiandaca MS, Mapstone M, Federoff H, Maddens M, Graham SF. A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning. J Alzheimers Dis 2020; 78:1381-1392. [PMID: 33164929 DOI: 10.3233/jad-200305] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally invasive, cost friendly, and easily accessible tool to diagnose AD, assess disease severity, and prognosticate course. Metabolomics is a promising tool for discovery of new, biologically, and clinically relevant biomarkers for AD detection and classification. OBJECTIVE Utilizing artificial intelligence and machine learning, we aim to assess whether a panel of metabolites as detected in plasma can be used as an objective and clinically feasible tool for the diagnosis of mild cognitive impairment (MCI) and AD. METHODS Using a community-based sample cohort acquired from different sites across the US, we adopted an approach combining Proton Nuclear Magnetic Resonance Spectroscopy (1H NMR), Liquid Chromatography coupled with Mass Spectrometry (LC-MS) and various machine learning statistical approaches to identify a biomarker panel capable of identifying those patients with AD and MCI from healthy controls. RESULTS Of the 212 measured metabolites, 5 were identified as optimal to discriminate between controls, and individuals with MCI or AD. Our models performed with AUC values in the range of 0.72-0.76, with the sensitivity and specificity values ranging from 0.75-0.85 and 0.69-0.81, respectively. Univariate and pathway analysis identified lipid metabolism as the most perturbed biochemical pathway in MCI and AD. CONCLUSION A comprehensive method of acquiring metabolomics data, coupled with machine learning techniques, has identified a strong panel of diagnostic biomarkers capable of identifying individuals with MCI and AD. Further, our data confirm what other groups have reported, that lipid metabolism is significantly perturbed in those individuals suffering with dementia. This work may provide additional insight into AD pathogenesis and encourage more in-depth analysis of the AD lipidome.
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Affiliation(s)
- Ali Yilmaz
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA.,Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - Ilyas Ustun
- Wayne State University, Civil and Environmental Engineering, Detroit, MI, USA
| | - Zafer Ugur
- Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - Sumeyya Akyol
- Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
| | - William T Hu
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Massimo S Fiandaca
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Mark Mapstone
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Howard Federoff
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - Michael Maddens
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA
| | - Stewart F Graham
- Department of Obstetrics and Gynecology, Department of Internal Medicine, Oakland University-William Beaumont School of Medicine, Rochester, MI, USA.,Metabolomics Division, Beaumont Research Institute, Royal Oak, MI USA
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Metabolic Profiling of CSF from People Suffering from Sporadic and LRRK2 Parkinson's Disease: A Pilot Study. Cells 2020; 9:cells9112394. [PMID: 33142859 PMCID: PMC7693941 DOI: 10.3390/cells9112394] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/15/2020] [Accepted: 10/20/2020] [Indexed: 01/21/2023] Open
Abstract
CSF from unique groups of Parkinson’s disease (PD) patients was biochemically profiled to identify previously unreported metabolic pathways linked to PD pathogenesis, and novel biochemical biomarkers of the disease were characterized. Utilizing both 1H NMR and DI-LC-MS/MS we quantitatively profiled CSF from patients with sporadic PD (n = 20) and those who are genetically predisposed (LRRK2) to the disease (n = 20), and compared those results with age and gender-matched controls (n = 20). Further, we systematically evaluated the utility of several machine learning techniques for the diagnosis of PD. 1H NMR and mass spectrometry-based metabolomics, in combination with bioinformatic analyses, provided useful information highlighting previously unreported biochemical pathways and CSF-based biomarkers associated with both sporadic PD (sPD) and LRRK2 PD. Results of this metabolomics study further support our group’s previous findings identifying bile acid metabolism as one of the major aberrant biochemical pathways in PD patients. This study demonstrates that a combination of two complimentary techniques can provide a much more holistic view of the CSF metabolome, and by association, the brain metabolome. Future studies for the prediction of those at risk of developing PD should investigate the clinical utility of these CSF-based biomarkers in more accessible biomatrices. Further, it is essential that we determine whether the biochemical pathways highlighted here are recapitulated in the brains of PD patients with the aim of identifying potential therapeutic targets.
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Abstract
Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.
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Richhariya B, Tanveer M. Least squares projection twin support vector clustering (LSPTSVC). Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Ontiveros E, Melin P, Castillo O. Designing hybrid classifiers based on general type-2 fuzzy logic and support vector machines. Soft comput 2020. [DOI: 10.1007/s00500-020-05052-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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