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Liu X, Chen J, Du Y, Tian Q, Wang L, Li W, Liu G, Tan Q, Wang J, Deng X. The changes of neurogenesis in the hippocampal dentate gyrus of SAMP8 mice and the effects of acupuncture and moxibustion. Brain Res 2024; 1831:148814. [PMID: 38395250 DOI: 10.1016/j.brainres.2024.148814] [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: 11/29/2023] [Revised: 02/15/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND Influenced by the global aging population, the incidence of Alzheimer's disease (AD) has increased sharply. In addition to increasing β-amyloid plaque deposition and tau tangle formation, neurogenesis dysfunction has recently been observed in AD. Therefore, promoting regeneration to improve neurogenesis and cognitive dysfunction can play an effective role in AD treatment. Acupuncture and moxibustion have been widely used in the clinical treatment of neurodegenerative diseases because of their outstanding advantages such as early, functional, and benign two-way adjustment. It is urgent to clarify the effectiveness, greenness, and safety of acupuncture and moxibustion in promoting neurogenesis in AD treatment. METHODS Senescence-accelerated mouse prone 8 (SAMP8) mice at various ages were used as experimental models to simulate the pathology and behaviors of AD mice. Behavioral experiments, immunohistochemistry, Western blot, and immunofluorescence experiments were used for comparison between different groups. RESULTS Acupuncture and moxibustion could increase the number of PCNA+ DCX+ cells, Nissl bodies, and mature neurons in the hippocampal Dentate gyrus (DG) of SAMP8 mice, restore the hippocampal neurogenesis, delay the AD-related pathological presentation, and improve the learning and memory abilities of SAMP8 mice. CONCLUSION The pathological process underlying AD and cognitive impairment were changed positively by improving the dysfunction of neurogenesis. This indicates the promising role of acupuncture and moxibustion in the prevention and treatment of AD.
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Affiliation(s)
- Xinyuan Liu
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China
| | - Jiangmin Chen
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China
| | - Yanjun Du
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China; Hubei Shizhen Laboratory, China; Hubei International Science and Technology Cooperation Base of Preventive Treatment by Acupuncture and Moxibustion, China.
| | - Qing Tian
- The Institute for Brain Research, Collaborative Innovation Center for Brain Science, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Li Wang
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China
| | - Weixian Li
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China
| | - Guangya Liu
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China
| | - Qian Tan
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China
| | - Jingzhi Wang
- College of Acupuncture-Moxibustion and Orthopaedics, Hubei University of Chinese Medicine, Wuhan, Hubei 430061, China
| | - Xiaoni Deng
- Wuhan University of Bioengineering, Wuhan, Hubei 430030, China
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2
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Mahmud T, Barua K, Habiba SU, Sharmen N, Hossain MS, Andersson K. An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep Transfer Learning. Diagnostics (Basel) 2024; 14:345. [PMID: 38337861 PMCID: PMC10855149 DOI: 10.3390/diagnostics14030345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/12/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer's disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer's diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model's exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer's disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.
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Affiliation(s)
- Tanjim Mahmud
- Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati 4500, Bangladesh
| | - Koushick Barua
- Department of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati 4500, Bangladesh
| | - Sultana Umme Habiba
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh;
| | - Nahed Sharmen
- Department of Obstetrics and Gynecology, Chattogram Maa-O-Shishu Hospital Medical College, Chittagong 4100, Bangladesh;
| | - Mohammad Shahadat Hossain
- Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh;
| | - Karl Andersson
- Pervasive and Mobile Computing Laboratory, Luleå University of Technology, 97187 Luleå, Sweden;
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Khaled M, Al-Jamal H, Tajer L, El-Mir R. Alzheimer's Disease in Lebanon: Exploring Genetic and Environmental Risk Factors-A Comprehensive Review. J Alzheimers Dis 2024; 99:21-40. [PMID: 38640157 DOI: 10.3233/jad-231432] [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] [Indexed: 04/21/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition that displays a high prevalence in Lebanon causing a local burden in healthcare and socio-economic sectors. Unfortunately, the lack of prevalence studies and clinical trials in Lebanon minimizes the improvement of AD patient health status. In this review, we include over 155 articles to cover the different aspects of AD ranging from mechanisms to possible treatment and management tools. We highlight some important modifiable and non-modifiable risk factors of the disease including genetics, age, cardiovascular diseases, smoking, etc. Finally, we propose a hypothetical genetic synergy model between APOE4 and TREM2 genes which constitutes a potential early diagnostic tool that helps in reducing the risk of AD based on preventative measures decades before cognitive decline. The studies on AD in Lebanon and the Middle East are scarce. This review points out the importance of genetic mapping in the understanding of disease pathology which is crucial for the emergence of novel diagnostic tools. Hence, we establish a rigid basis for further research to identify the most influential genetic and environmental risk factors for the purpose of using more specific diagnostic tools and possibly adopting a local management protocol.
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Affiliation(s)
| | - Hadi Al-Jamal
- Faculty of Public Health III, Lebanese University, Tripoli, Lebanon
| | - Layla Tajer
- Faculty of Public Health III, Lebanese University, Tripoli, Lebanon
| | - Reem El-Mir
- Faculty of Public Health III, Lebanese University, Tripoli, Lebanon
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4
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Rodríguez Mallma MJ, Vilca-Aguilar M, Zuloaga-Rotta L, Borja-Rosales R, Salas-Ojeda M, Mauricio D. Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS). Diagnostics (Basel) 2023; 14:22. [PMID: 38201331 PMCID: PMC10871108 DOI: 10.3390/diagnostics14010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/14/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024] Open
Abstract
A cerebral arteriovenous malformation (AVM) is a tangle of abnormal blood vessels that irregularly connects arteries and veins. Stereotactic radiosurgery (SRS) has been shown to be an effective treatment for AVM patients, but the factors associated with AVM obliteration remains a matter of debate. In this study, we aimed to develop a model that can predict whether patients with AVM will be cured 36 months after intervention by means of SRS and identify the most important predictors that explain the probability of being cured. A machine learning (ML) approach was applied using decision tree (DT) and logistic regression (LR) techniques on historical data (sociodemographic, clinical, treatment, angioarchitecture, and radiosurgery procedure) of 202 patients with AVM who underwent SRS at the Instituto de Radiocirugía del Perú (IRP) between 2005 and 2018. The LR model obtained the best results for predicting AVM cure with an accuracy of 0.92, sensitivity of 0.93, specificity of 0.89, and an area under the curve (AUC) of 0.98, which shows that ML models are suitable for predicting the prognosis of medical conditions such as AVM and can be a support tool for medical decision-making. In addition, several factors were identified that could explain whether patients with AVM would be cured at 36 months with the highest likelihood: the location of the AVM, the occupation of the patient, and the presence of hemorrhage.
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Affiliation(s)
| | - Marcos Vilca-Aguilar
- Instituto de Radiocirugía del Perú, Clínica San Pablo, Lima 15023, Peru
- Servicio de Neurocirugía, Hospital María Auxiliadora, Lima 15828, Peru
| | - Luis Zuloaga-Rotta
- Facultad de Ingeniería Industrial y de Sistemas, Universidad Nacional de Ingeniería, Lima 15333, Peru
| | - Rubén Borja-Rosales
- Facultad de Ingeniería Industrial y de Sistemas, Universidad Nacional de Ingeniería, Lima 15333, Peru
| | | | - David Mauricio
- Universidad Nacional Mayor de San Marcos, Lima 15081, Peru
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Jahan S, Abu Taher K, Kaiser MS, Mahmud M, Rahman MS, Hosen ASMS, Ra IH. Explainable AI-based Alzheimer's prediction and management using multimodal data. PLoS One 2023; 18:e0294253. [PMID: 37972072 PMCID: PMC10653516 DOI: 10.1371/journal.pone.0294253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world's elderly people. Day by day the number of Alzheimer's patients is rising. Considering the increasing rate and the dangers, Alzheimer's disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer's diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. OBJECTIVE To solve these issues, this paper proposes a novel explainable Alzheimer's disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer's disease. METHOD For predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work. RESULTS AND CONCLUSIONS The performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer's disease, cognitively normal, non-Alzheimer's dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer's disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer's patient management architecture is also proposed in this work.
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Affiliation(s)
- Sobhana Jahan
- Department of Computer Science and Engineering, Bangladesh University of Professionals, Dhaka, Bangladesh
- Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh
| | - Kazi Abu Taher
- Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh
| | - M. Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Nottingham, United Kingdom
| | - Md. Sazzadur Rahman
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
| | - A. S. M. Sanwar Hosen
- Department of Artificial Intelligence and Big Data, Woosong University, Daejeon, South Korea
| | - In-Ho Ra
- School of Computer, Information and Communication Engineering, Kunsan National University, Gunsan, South Korea
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6
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Peng Q, Zeng Q, Wang F, Wu X, Zhang R, Shi G, Zhang M. Multi-engineered Graphene Extended-Gate Field-Effect Transistor for Peroxynitrite Sensing in Alzheimer's Disease. ACS NANO 2023; 17:21984-21992. [PMID: 37874899 DOI: 10.1021/acsnano.3c08499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
The expression of β-amyloid peptides (Aβ), a pathological indicator of Alzheimer's disease (AD), was reported to be inapparent in the early stage of AD. While peroxynitrite (ONOO-) is produced excessively and emerges earlier than Aβ plaques in the progression of AD, it is thus significant to sensitively detect ONOO- for early diagnosis of AD and its pathological research. Herein, we unveiled an integrated sensor for monitoring ONOO-, which consisted of a commercially available field-effect transistor (FET) and a high-performance multi-engineered graphene extended-gate (EG) electrode. In the configuration of the presented EG electrode, laser-induced graphene (LIG) intercalated with MnO2 nanoparticles (MnO2/LIG) can improve the electrical properties of LIG and the sensitivity of the sensor, and graphene oxide (GO)-MnO2/Hemin nanozyme with ONOO- isomerase activity can selectively trigger the isomerization of ONOO- to NO3-. With this synergistic effect, our EG-FET sensor can respond to the ONOO- with high sensitivity and selectivity. Moreover, taking advantage of our EG-FET sensor, we modularly assembled a portable sensing platform for wireless tracking ONOO- levels in the brain tissue of AD transgenic mice at earlier stages before massive Aβ plaques appeared, and we systematically explored the complex role of ONOO- in the occurrence and development of AD.
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Affiliation(s)
- Qiwen Peng
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
| | - Qiankun Zeng
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
| | - Fangbing Wang
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
| | - Xiaoyuan Wu
- School of Communication and Electric Engineering, East China Normal University, Shanghai 200241, China
| | - Runxi Zhang
- School of Communication and Electric Engineering, East China Normal University, Shanghai 200241, China
| | - Guoyue Shi
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
| | - Min Zhang
- School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai 200241, China
- Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing 401120, China
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7
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Rodrigues PM, Bispo BC, Freitas D, Lobo Marques JA, Teixeira JP. Editorial: Advances in machine learning approaches and technologies for supporting nervous system disease diagnosis. Front Hum Neurosci 2023; 17:1295074. [PMID: 37900723 PMCID: PMC10613104 DOI: 10.3389/fnhum.2023.1295074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Affiliation(s)
- Pedro Miguel Rodrigues
- CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Bruno Catarino Bispo
- Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil
| | - Diamantino Freitas
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Porto, Portugal
| | | | - João Paulo Teixeira
- CEDRI—Research Centre in Digitalization and Intelligent Robotics and SusTEC—Associate Laboratory for Sustainability and Technology, Instituto Politécnico de Bragança, Bragança, Portugal
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8
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Ahmad I, Siddiqi MH, Alhujaili SF, Alrowaili ZA. Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA. Healthcare (Basel) 2023; 11:2551. [PMID: 37761748 PMCID: PMC10530944 DOI: 10.3390/healthcare11182551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.
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Affiliation(s)
- Irshad Ahmad
- Department of Computer Science, Islamia College, Peshawar 25000, KPK, Pakistan
| | - Muhammad Hameed Siddiqi
- College of Computer and Information Sciences, Jouf University, Sakaka 2014, Aljouf, Saudi Arabia
| | | | - Ziyad Awadh Alrowaili
- Department of Physics, College of Science, Jouf University, Sakaka 2014, Aljouf, Saudi Arabia
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9
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Saleh H, Elrashidy N, Elaziz MA, Aseeri AO, El-sappagh S. Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data.. [DOI: 10.21203/rs.3.rs-3250006/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression.
We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.
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Affiliation(s)
- Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt
| | - Nora ElRashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh, 13518, Egypt
| | - Mohamed Abd Elaziz
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
| | - Ahmad O. Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
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Salehi W, Baglat P, Gupta G, Khan SB, Almusharraf A, Alqahtani A, Kumar A. An Approach to Binary Classification of Alzheimer's Disease Using LSTM. Bioengineering (Basel) 2023; 10:950. [PMID: 37627835 PMCID: PMC10451729 DOI: 10.3390/bioengineering10080950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/25/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.
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Affiliation(s)
- Waleed Salehi
- Yogananda School of AI, Shoolini University, Bajhol 173229, India; (W.S.); (G.G.)
| | - Preety Baglat
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), University of Madeira, 9000-082 Funchal, Portugal;
| | - Gaurav Gupta
- Yogananda School of AI, Shoolini University, Bajhol 173229, India; (W.S.); (G.G.)
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK;
| | - Ahlam Almusharraf
- Department of Business Administration, College of Business and Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Ali Alqahtani
- Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia;
| | - Adarsh Kumar
- School of Computer Science, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India
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11
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Mirkin S, Albensi BC. Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease? Front Aging Neurosci 2023; 15:1094233. [PMID: 37187577 PMCID: PMC10177660 DOI: 10.3389/fnagi.2023.1094233] [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: 11/09/2022] [Accepted: 03/27/2023] [Indexed: 05/17/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.
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Affiliation(s)
- Sophia Mirkin
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Benedict C. Albensi
- Barry and Judy Silverman College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, United States
- St. Boniface Hospital Research, Winnipeg, MB, Canada
- University of Manitoba, Winnipeg, MB, Canada
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12
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Uddin KMM, Alam MJ, Jannat-E-Anawar, Uddin MA, Aryal S. A Novel Approach Utilizing Machine Learning for the Early Diagnosis of Alzheimer's Disease. BIOMEDICAL MATERIALS & DEVICES (NEW YORK, N.Y.) 2023; 1:1-17. [PMID: 37363136 PMCID: PMC10088738 DOI: 10.1007/s44174-023-00078-9] [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] [Received: 02/02/2023] [Accepted: 03/27/2023] [Indexed: 06/28/2023]
Abstract
Alzheimer's disease (AD) is one of the leading causes of dementia among older people. In addition, a considerable portion of the world's population suffers from metabolic problems, such as Alzheimer's disease and diabetes. Alzheimer's disease affects the brain in a degenerative manner. As the elderly population grows, this illness can cause more people to become inactive by impairing their memory and physical functionality. This might impact their family members and the financial, economic, and social spheres. Researchers have recently investigated different machine learning and deep learning approaches to detect such diseases at an earlier stage. Early diagnosis and treatment of AD help patients to recover from it successfully and with the least harm. This paper proposes a machine learning model that comprises GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost to predict Alzheimer's disease. The model is trained using the open access series of imaging studies (OASIS) dataset to evaluate the performance in terms of accuracy, precision, recall, and F1 score. Our findings showed that the voting classifier attained the highest validation accuracy of 96% for the AD dataset. Therefore, ML algorithms have the potential to drastically lower Alzheimer's disease annual mortality rates through accurate detection.
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Affiliation(s)
| | - Mir Jafikul Alam
- Department of Computer Science and Engineering, Dhaka International University, Dhaka, 1205 Bangladesh
| | - Jannat-E-Anawar
- Department of Computer Science and Engineering, Dhaka International University, Dhaka, 1205 Bangladesh
| | - Md Ashraf Uddin
- School of Information and Technology, Deakin University, Warun Ponds, Geelong, Australia
| | - Sunil Aryal
- School of Information and Technology, Deakin University, Warun Ponds, Geelong, Australia
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Shastry KA, Sattar SA. Logistic random forest boosting technique for Alzheimer’s diagnosis. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY 2023; 15:1719-1731. [PMID: 37056794 PMCID: PMC9983513 DOI: 10.1007/s41870-023-01187-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 02/16/2023] [Indexed: 03/06/2023]
Abstract
Alzheimer's disease (AD) is a common and well-known neurodegenerative condition that causes cognitive impairment. In the field of medicine, it is the "nervous system" disorder that has received the most attention. Despite this extensive research, there is no treatment or strategy to slow or stop its spread. Nevertheless, there are a variety of options (medication and non-medication alternatives) that may aid in the treatment of AD symptoms at their various phases, thereby enhancing the patient's quality of life. As AD advances over time, it is necessary to treat patients at their various stages appropriately. As a result, detecting and classifying AD phases prior to symptom treatment can be beneficial. Approximately twenty years ago, the rate of progress in the field of machine learning (ML) accelerated dramatically. Using ML methods, this study focuses on early AD identification. The "Alzheimer's Disease Neuroimaging Initiative" (ADNI) dataset was subjected to exhaustive testing for AD identification. The purpose was to classify the dataset into three groups: AD, "Cognitive Normal" (CN), and "Late Mild Cognitive Impairment" (LMCI). In this paper, we present the ensemble model Logistic Random Forest Boosting (LRFB), representing the ensemble of “Logistic Regression” (LR), “Random Forest” (RF), and “Gradient Boost” (GB). The proposed LRFB outperformed LR, RF, GB, “k-Nearest Neighbour” (k-NN), “Multi-Layer Perceptron” (MLP), “Support Vector Machine” (SVM), “AdaBoost” (AB), “Naïve Bayes” (NB), “XGBoost” (XGB), “Decision Tree” (DT), and other ensemble ML models with respect to the performance metrics “Accuracy” (Acc), “Recall” (Rec), “Precision” (Prec), and “F1-Score” (FS).
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Khan YF, Kaushik B, Chowdhary CL, Srivastava G. Ensemble Model for Diagnostic Classification of Alzheimer's Disease Based on Brain Anatomical Magnetic Resonance Imaging. Diagnostics (Basel) 2022; 12:diagnostics12123193. [PMID: 36553199 PMCID: PMC9777931 DOI: 10.3390/diagnostics12123193] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/08/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022] Open
Abstract
Alzheimer's is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain's anatomy and enables the identification of diagnostic features. Artificial intelligence (AI) in neuroimaging has the potential to significantly enhance the treatment process for Alzheimer's disease (AD). The objective of this study is two-fold: (1) to compare existing Machine Learning (ML) algorithms for the classification of AD. (2) To propose an effective ensemble-based model for the same and to perform its comparative analysis. In this study, data from the Alzheimer's Diseases Neuroimaging Initiative (ADNI), an online repository, is utilized for experimentation consisting of 2125 neuroimages of Alzheimer's disease (n = 975), mild cognitive impairment (n = 538) and cognitive normal (n = 612). For classification, the framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN) followed by some variations of Support Vector Machine (SVM), such as SVM (RBF kernel), SVM (Polynomial Kernel), and SVM (Sigmoid kernel), as well as Gradient Boost (GB), Extreme Gradient Boosting (XGB) and Multi-layer Perceptron Neural Network (MLP-NN). Afterwards, an Ensemble Based Generic Kernel is presented where Master-Slave architecture is combined to attain better performance. The proposed model is an ensemble of Extreme Gradient Boosting, Decision Tree and SVM_Polynomial kernel (XGB + DT + SVM). At last, the proposed method is evaluated using cross-validation using statistical techniques along with other ML models. The presented ensemble model (XGB + DT + SVM) outperformed existing state-of-the-art algorithms with an accuracy of 89.77%. The efficiency of all the models was optimized using Grid-based tuning, and the results obtained after such process showed significant improvement. XGB + DT + SVM with optimized parameters outperformed all other models with an efficiency of 95.75%. The implication of the proposed ensemble-based learning approach clearly shows the best results compared to other ML models. This experimental comparative analysis improved understanding of the above-defined methods and enhanced their scope and significance in the early detection of Alzheimer's disease.
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Affiliation(s)
| | - Baijnath Kaushik
- School of CSE, Shri Mata Vaishno Devi University, Katra 182320, India
| | - Chiranji Lal Chowdhary
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
- Correspondence:
| | - Gautam Srivastava
- Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
- Research Centre for Interneural Computing, China Medical University, Taichung 40402, Taiwan
- Department of Computer Science and Math, Lebanese American University, Beirut 1102, Lebanon
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Hai T, Zhou J, Srividhya SR, Jain SK, Young P, Agrawal S. BVFLEMR: an integrated federated learning and blockchain technology for cloud-based medical records recommendation system. JOURNAL OF CLOUD COMPUTING: ADVANCES, SYSTEMS AND APPLICATIONS 2022. [DOI: 10.1186/s13677-022-00294-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractBlockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.
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Francis A, Pandian IA, Anitha J. A boon to aged society: Early diagnosis of Alzheimer's disease-An opinion. Front Public Health 2022; 10:1076472. [PMID: 36530651 PMCID: PMC9751990 DOI: 10.3389/fpubh.2022.1076472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 11/14/2022] [Indexed: 12/04/2022] Open
Affiliation(s)
- Ambily Francis
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India,Department of Electronics and Communication Engineering, Sahrdaya College of Engineering and Technology, Kodakara, India
| | - Immanuel Alex Pandian
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - J. Anitha
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India,*Correspondence: J. Anitha
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Hajjo R, Sabbah DA, Abusara OH, Al Bawab AQ. A Review of the Recent Advances in Alzheimer's Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics. Diagnostics (Basel) 2022; 12:diagnostics12122975. [PMID: 36552984 PMCID: PMC9777434 DOI: 10.3390/diagnostics12122975] [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: 10/16/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2022] Open
Abstract
Alzheimer's disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer's disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer's disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments.
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Affiliation(s)
- Rima Hajjo
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA
- National Center for Epidemics and Communicable Disease Control, Amman 11118, Jordan
- Correspondence:
| | - Dima A. Sabbah
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Osama H. Abusara
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Abdel Qader Al Bawab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
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Richardson A, Robbins CB, Wisely CE, Henao R, Grewal DS, Fekrat S. Artificial intelligence in dementia. Curr Opin Ophthalmol 2022; 33:425-431. [PMID: 35916570 DOI: 10.1097/icu.0000000000000881] [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: 11/26/2022]
Abstract
PURPOSE OF REVIEW Artificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms. A comprehensive review of emerging trends will allow physicians and other healthcare providers to better anticipate and understand these powerful tools. RECENT FINDINGS Machine learning models that utilize cerebral biomarkers are demonstrably effective for dementia identification and prediction; however, cerebral biomarkers are relatively expensive and not widely available. As eye images harbor several ophthalmic biomarkers that mirror the state of the brain and can be clinically observed with routine imaging, eye-based machine learning models are an emerging area, with efficacy comparable with cerebral-based machine learning models. Emerging machine learning architectures like recurrent, convolutional, and partially pretrained neural networks have proven to be promising frontiers for feature extraction and classification with ocular biomarkers. SUMMARY Machine learning models that can accurately distinguish those with symptomatic Alzheimer's dementia from those with mild cognitive impairment and normal cognition as well as predict progressive disease using relatively inexpensive and accessible ocular imaging inputs are impactful tools for the diagnosis and risk stratification of Alzheimer's dementia continuum. If these machine learning models can be incorporated into clinical care, they may simplify diagnostic efforts. Recent advancements in ocular-based machine learning efforts are promising steps forward.
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Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1051388. [PMID: 35685134 PMCID: PMC9173933 DOI: 10.1155/2022/1051388] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/15/2022] [Accepted: 05/03/2022] [Indexed: 12/18/2022]
Abstract
Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world.
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A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks. SENSORS 2022; 22:s22124475. [PMID: 35746256 PMCID: PMC9228907 DOI: 10.3390/s22124475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/30/2022] [Accepted: 06/09/2022] [Indexed: 12/04/2022]
Abstract
Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.
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A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing. SUSTAINABILITY 2022. [DOI: 10.3390/su14106256] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.
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Abstract
Hadoop is a framework for storing and processing huge amounts of data. With HDFS, large data sets can be managed on commodity hardware. MapReduce is a programming model for processing vast amounts of data in parallel. Mapping and reducing can be performed by using the MapReduce programming framework. A very large amount of data is transferred from Mapper to Reducer without any filtering or recursion, resulting in overdrawn bandwidth. In this paper, we introduce an algorithm called Inner MAPping Combiner (IMapC) for the map phase. This algorithm in the Mapper combines the values of recurring keys. In order to test the efficiency of the algorithm, different approaches were tested. According to the test, MapReduce programs that are implemented with the Default Combiner (DC) of IMapC will be 70% more efficient than those that are implemented without one. To make computations significantly faster, this work can be combined with MapReduce.
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