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Ejiyi CJ, Cai D, Ejiyi MB, Chikwendu IA, Coker K, Oluwasanmi A, Bamisile OF, Ejiyi TU, Qin Z. Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models. Comput Biol Med 2024; 182:109168. [PMID: 39342675 DOI: 10.1016/j.compbiomed.2024.109168] [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: 06/28/2024] [Revised: 09/17/2024] [Accepted: 09/17/2024] [Indexed: 10/01/2024]
Abstract
Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanations analysis on enhancing the performance and interpretability of machine learning models for liver disease classification. Our results demonstrate significant improvements in accuracy, precision, recall, F1_score, and Matthews correlation coefficient across various algorithms when polynomial- SHapley Additive exPlanations analysis is applied. Specifically, the Light Gradient Boosting Machine model achieves exceptional performance with 100 % accuracy in both scenarios. Furthermore, by comparing the results obtained with and without the approach, we observe substantial differences in the performance, highlighting the importance of incorporating Polynomial-SHapley Additive exPlanations analysis for improved model performance. The Polynomial features and SHapley Additive exPlanations values also enhance the interpretability of machine learning models by capturing complex feature interactions, enabling users to gain deeper insights into the underlying mechanisms driving the diagnosis. Moreover, data rebalancing using Synthetic Minority Over-sampling Technique and parameter tuning were employed to optimize the performance of the models. These findings underscore the significance of employing this analytical approach in machine-learning-based diagnostic systems for liver diseases, offering superior performance and enhanced interpretability for informed decision-making in clinical practice.
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Affiliation(s)
- Chukwuebuka Joseph Ejiyi
- College of Nuclear Technology and Automation Engineering, Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Sichuan, Chengdu, China; Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Dongsheng Cai
- College of Nuclear Technology and Automation Engineering, Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Sichuan, Chengdu, China.
| | - Makuachukwu B Ejiyi
- Pharmacy Department, University of Nigeria Nsukka, Nsukka, Enugu State, Nigeria
| | - Ijeoma A Chikwendu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Kenneth Coker
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China; Department of Electrical and Electronic Engineering, Ho Technical University Ghana, Ghana
| | - Ariyo Oluwasanmi
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Oluwatoyosi F Bamisile
- College of Nuclear Technology and Automation Engineering, Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Sichuan, Chengdu, China
| | - Thomas U Ejiyi
- Department of Pure and Industrial Chemistry, University of Nigeria Nsukka, Nsukka, Enugu State, Nigeria
| | - Zhen Qin
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
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Khater AA, Gaballah EM, El-Bardin M, El-Nagar AM. Real time adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural network proportional-integral-derivative controller for nonlinear systems. ISA TRANSACTIONS 2024; 152:191-207. [PMID: 38945764 DOI: 10.1016/j.isatra.2024.06.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 06/22/2024] [Accepted: 06/22/2024] [Indexed: 07/02/2024]
Abstract
This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties.
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Affiliation(s)
- A Aziz Khater
- Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menof 32852, Egypt
| | - Eslam M Gaballah
- Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menof 32852, Egypt
| | - Mohammad El-Bardin
- Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menof 32852, Egypt
| | - Ahmad M El-Nagar
- Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menof 32852, Egypt.
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Wu D, Yin H, Yang C, Zhang Z, Fang F, Wang J, Li X, Xie Y, Hu X, Zhuo R, Chen Y, Yu J, Li T, Li G, Pan J. The BET PROTAC inhibitor GNE-987 displays anti-tumor effects by targeting super-enhancers regulated gene in osteosarcoma. BMC Cancer 2024; 24:928. [PMID: 39090568 PMCID: PMC11292958 DOI: 10.1186/s12885-024-12691-y] [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: 11/06/2023] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Osteosarcoma (OS) is one of the most common primary malignant tumors of bone in children, which develops from osteoblasts and typically occurs during the rapid growth phase of the bone. Recently, Super-Enhancers(SEs)have been reported to play a crucial role in osteosarcoma growth and metastasis. Therefore, there is an urgent need to identify specific targeted inhibitors of SEs to assist clinical therapy. This study aimed to elucidate the role of BRD4 inhibitor GNE-987 targeting SEs in OS and preliminarily explore its mechanism. METHODS We evaluated changes in osteosarcoma cells following treatment with a BRD4 inhibitor GNE-987. We assessed the anti-tumor effect of GNE-987 in vitro and in vivo by Western blot, CCK8, flow cytometry detection, clone formation, xenograft tumor size measurements, and Ki67 immunohistochemical staining, and combined ChIP-seq with RNA-seq techniques to find its anti-tumor mechanism. RESULTS In this study, we found that extremely low concentrations of GNE-987(2-10 nM) significantly reduced the proliferation and survival of OS cells by degrading BRD4. In addition, we found that GNE-987 markedly induced cell cycle arrest and apoptosis in OS cells. Further study indicated that VHL was critical for GNE-987 to exert its antitumor effect in OS cells. Consistent with in vitro results, GNE-987 administration significantly reduced tumor size in xenograft models with minimal toxicity, and partially degraded the BRD4 protein. KRT80 was identified through analysis of the RNA-seq and ChIP-seq data. U2OS HiC analysis suggested a higher frequency of chromatin interactions near the KRT80 binding site. The enrichment of H3K27ac modification at KRT80 was significantly reduced after GNE-987 treatment. KRT80 was identified as playing an important role in OS occurrence and development. CONCLUSIONS This research revealed that GNE-987 selectively degraded BRD4 and disrupted the transcriptional regulation of oncogenes in OS. GNE-987 has the potential to affect KRT80 against OS.
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Affiliation(s)
- Di Wu
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Hongli Yin
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Chun Yang
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Zimu Zhang
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Fang Fang
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Jianwei Wang
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Xiaolu Li
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Yi Xie
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Xiaohan Hu
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Ran Zhuo
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Yanling Chen
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Juanjuan Yu
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Tiandan Li
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China
| | - Gen Li
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China.
| | - Jian Pan
- Institute of Pediatric Research, Children's Hospital of Soochow University, No.92 Zhongnan Street, SIP, Suzhou, 215003, China.
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Seifpanahi MS, Ghaemi T, Ghaleiha A, Sobhani-Rad D, Zarabian MK. The Association between Depression Severity, Prosody, and Voice Acoustic Features in Women with Depression. ScientificWorldJournal 2023; 2023:9928446. [PMID: 38089742 PMCID: PMC10715859 DOI: 10.1155/2023/9928446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/19/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
The aim was to define the association between the severity of depression, prosody, and voice acoustic features in women suffering from depression and its comparisons with nondepressed people. Prosody and acoustic features in 30 women with major depression hospitalized in a psychiatric ward and 30 healthy women were investigated in a cross-sectional study. To define the severity of depression, the Hamilton Rating Scale for Depression (HRS-D) was applied. Acoustic parameters such as jitter, shimmer, cepstral peak prominence (CPP), standard deviation of fundamental frequency (SD F0), harmonic-to-noise ratio, and F0 and also some speech prosodic features including the speed of speech, switching pause duration means, and durations of produced sentences with different modals were measured quantitatively. Also, six raters judged the patient's prosody qualitatively. SPSS V.28 was used for all statistical analyses (p < 0.05). There was a significant correlation between HRS-D with jitter, SD F0, speed of speech, and switching pause means (p ≤ 0.05). The means of CPP and duration of producing emotional sentences differed between the depression and control groups. The HRS-D scores were significantly correlated with switching pauses in patients (Pearson coefficient = 0.47, p=0.05). The results of the perceptual evaluation of prosody judged by six raters showed an 85% correlation between them (p ≤ 0.001). Some acoustic and prosodic parameters are different between healthy women and those with depression disorder (e.g., CPP and duration of emotional sentences) and may also have an association with the severity of depression (e.g., jitter, SD F0, speed of speech, and switching pause means) in women with depression disorder. It was indicated that the best sentence modal to assess prosody in patients with depression would be exclamatory ones compared to declarative and interrogative sentences.
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Affiliation(s)
- Mohammad-Sadegh Seifpanahi
- Department of Speech and Language Pathology, Autism Spectrum Disorders Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Tina Ghaemi
- Department of Linguistic, The University of Konstanz, Konstanz, Germany
| | - Ali Ghaleiha
- Department of Psychiatry, Research Center for Behavioral Disorders and Substance Abuse, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Davood Sobhani-Rad
- Department of Speech Therapy, School of Paramedical Science, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad-Kazem Zarabian
- Research Center for Behavioral Disorders and Substance Abuse, Hamadan University of Medical Sciences, Hamadan, Iran
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Yang J, Xu X, Ma X, Wang Z, You Q, Shan W, Yang Y, Bo X, Yin C. Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:88431-88443. [PMID: 37438508 DOI: 10.1007/s11356-023-28682-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.
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Affiliation(s)
- Jing Yang
- Intersection of Wohushan Road and Wuhan Road in Beicheng New Area, Linyi People's Hospital, Linyi, 276000, Shandong, People's Republic of China
| | - Xin Xu
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Xiaotian Ma
- School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin City, 132022, People's Republic of China
| | - Zhaotong Wang
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Qian You
- School of Management and Engineering, Capital University of Economics and Business, Beijing, 100070, People's Republic of China
| | - Wanyue Shan
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Ying Yang
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Xin Bo
- Department of Environmental Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
- BUCT Institute for Carbon-Neutrality of Chinese Industries, Beijing, 100029, People's Republic of China
| | - Chuansheng Yin
- Intersection of Wohushan Road and Wuhan Road in Beicheng New Area, Linyi People's Hospital, Linyi, 276000, Shandong, People's Republic of China.
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Nilashi M, Abumalloh RA, Alyami S, Alghamdi A, Alrizq M. A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches. Diagnostics (Basel) 2023; 13:diagnostics13101821. [PMID: 37238305 DOI: 10.3390/diagnostics13101821] [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: 02/09/2023] [Revised: 03/10/2023] [Accepted: 04/12/2023] [Indexed: 05/28/2023] Open
Abstract
Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.
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Affiliation(s)
- Mehrbakhsh Nilashi
- UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia
- Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia (USM), George Town 11800, Malaysia
| | - Rabab Ali Abumalloh
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
| | - Sultan Alyami
- Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
| | - Abdullah Alghamdi
- Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
| | - Mesfer Alrizq
- Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
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7
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Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alghamdi A, Alrizq M, Alyami S, Nayer FK. Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. Heliyon 2023; 9:e15258. [PMID: 37101630 PMCID: PMC10123194 DOI: 10.1016/j.heliyon.2023.e15258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023] Open
Abstract
The analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning techniques. In previous studies, supervised learning techniques have been widely used in EEG signals analysis for eye state classification. Their main goal has been the improvement of classification accuracy through the use of novel algorithms. The trade-off between classification accuracy and computation complexity is an important task in EEG signals analysis. In this paper, a hybrid method that can handle multivariate signals and non-linear is proposed with supervised and un-supervised learning to achieve a fast EEG eye state classification with high prediction accuracy to provide real-time decision-making applicability. We use the Learning Vector Quantization (LVQ) technique and bagged tree techniques. The method was evaluated on a real-world EEG dataset which included 14976 instances after the removal of outlier instances. Using LVQ, 8 clusters were generated from the data. The bagged tree was applied on 8 clusters and compared with other classifiers. Our experiments revealed that LVQ combined with the bagged tree provides the best results (Accuracy = 0.9431) compared with the bagged tree, CART (Classification And Regression Tree) (Accuracy = 0.8200), LDA (Linear Discriminant Analysis) (Accuracy = 0.7931), Random Trees (Accuracy = 0.8311), Naïve Bayes (Accuracy = 0.8331) and Multilayer Perceptron (Accuracy = 0.7718), which demonstrates the effectiveness of incorporating ensemble learning and clustering approaches in the analysis of EEG signals. We also provided the time complexity of the methods for prediction speed (Observation/Second). The result showed that LVQ + Bagged Tree provides the best result for prediction speed (58942 Obs/Sec) in relation to Bagged Tree (28453 Obs/Sec), CART (27784 Obs/Sec), LDA (26435 Obs/Sec), Random Trees (27921), Naïve Bayes (27217) and Multilayer Perceptron (24163).
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Sachdeva RK, Bathla P, Rani P, Solanki V, Ahuja R. A systematic method for diagnosis of hepatitis disease using machine learning. INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2023; 19:71-80. [PMID: 36628173 PMCID: PMC9818056 DOI: 10.1007/s11334-022-00509-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Hepatitis is among the deadliest diseases on the planet. Machine learning approaches can contribute toward diagnosing hepatitis disease based on a few characteristics. On the UCI dataset, authors assessed distinct classifiers' performance in order to develop a systematic strategy for hepatitis disease diagnosis. The classifiers used are support vector machine, logistic regression (LR), K-nearest neighbor, and random forest. The classifiers were employed without class balancing and in conjunction with class balancing using SMOTE strategy. Both studies, classification without class balancing and with class balancing, were compared in terms of different performance parameters. After adopting class balancing, the efficiency of classifiers improved significantly. LR with SMOTE provided the highest level of accuracy (93.18%).
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Affiliation(s)
- Ravi Kumar Sachdeva
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
| | | | - Pooja Rani
- MMICTBM, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana India
| | - Vikas Solanki
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
| | - Rakesh Ahuja
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India
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Ghane M, Ang MC, Nilashi M, Sorooshian S. Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Jiang Q, Zhou X, Wang R, Ding W, Chu Y, Tang S, Jia X, Xu X. Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Rashid J, Batool S, Kim J, Wasif Nisar M, Hussain A, Juneja S, Kushwaha R. An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction. Front Public Health 2022; 10:860396. [PMID: 35433587 PMCID: PMC9008324 DOI: 10.3389/fpubh.2022.860396] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/22/2022] [Indexed: 12/23/2022] Open
Abstract
Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.
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Affiliation(s)
- Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Cheonan, South Korea
| | - Saba Batool
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Cheonan, South Korea
- *Correspondence: Jungeun Kim
| | - Muhammad Wasif Nisar
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Amir Hussain
- Data Science and Cyber Analytics Research Group, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Sapna Juneja
- Department of Computer Science, KIET Group of Institutions, Ghaziabad, India
| | - Riti Kushwaha
- Department of Computer Science, Bennett University, Greater Noida, India
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12
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Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2793361. [PMID: 35154618 PMCID: PMC8831050 DOI: 10.1155/2022/2793361] [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: 12/11/2021] [Revised: 01/13/2022] [Accepted: 01/15/2022] [Indexed: 01/12/2023]
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.
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13
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Guido R, Groccia MC, Conforti D. A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers. Soft comput 2022. [DOI: 10.1007/s00500-022-06768-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractIn machine learning, hyperparameter tuning is strongly useful to improve model performance. In our research, we concentrate our attention on classifying imbalanced data by cost-sensitive support vector machines. We propose a multi-objective approach that optimizes model’s hyper-parameters. The approach is devised for imbalanced data. Three SVM model’s performance measures are optimized. We present the algorithm in a basic version based on genetic algorithms, and as an improved version based on genetic algorithms combined with decision trees. We tested the basic and the improved approach on benchmark datasets either as serial and parallel version. The improved version strongly reduces the computational time needed for finding optimized hyper-parameters. The results empirically show that suitable evaluation measures should be used in assessing the classification performance of classification models with imbalanced data.
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Kalaivani K, Uma Maheswari N, Venkatesh R. Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier. NETWORK (BRISTOL, ENGLAND) 2022; 33:95-123. [PMID: 35465830 DOI: 10.1080/0954898x.2022.2061062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cardiac disease is the predominant cause of global death mainly due to its hidden symptoms and late diagnosis. Hence, early detection is important to improve quality of life. Though traditional researches attempted to predict heart disease, most of them lacked with respect to accuracy. To solve this, the present study proposes a hybridized Ant Lion Crow Search Optimization Genetic Algorithm (ALCSOGA) to perform effective feature selection. This hybrid optimization encompasses Ant Lion, Crow Search and Genetic Algorithm. Ant lion algorithm determines the elite position. While, the Crow Search Algorithm utilizes the phenomenon of position and memory of each crow for evaluating the objective function. Both these algorithms are fed into Genetic Algorithm to improve the performance of feature selection process. Then, Stochastic Learning rate optimized Long Short Term Memory (LSTM) is proposed to classify the extracted optimized features. Finally, comparative analysis is performed in terms of accuracy, recall, F1-score, and precision. Moreover, statistical analysis is performed with respect to Sum of Squares (SS), degree of freedom (df), F Critical (F crit), F Statistics (F), p, and Mean Square (MS) value. Analytical results revealed the efficiency of proposed system over conventional methods and thereby confirming its efficiency for predicting heart disease.
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Affiliation(s)
- K Kalaivani
- Sree Vidyanikethan Engineering College, Tirupati
| | - N Uma Maheswari
- Professor, Department of Computer Science and Engineering, P.s.n.a. College of Engineering and Technology, Dindigul, India
| | - R Venkatesh
- Professor, Department of Information Technology, Psna College of Engineering and Technology, Dindigul, India
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Chen Y, Zhang L, Wei M. How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China. Front Public Health 2022; 9:833687. [PMID: 35127633 PMCID: PMC8813850 DOI: 10.3389/fpubh.2021.833687] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 12/29/2021] [Indexed: 11/24/2022] Open
Abstract
With the emergence of the digital age, smart healthcare services based on the new generation of information technologies play an increasingly important role in improving the quality of resident health. This study empirically examined the impact of regional smart healthcare services on resident health as well as the underlying mechanism by employing a two-way fixed effects model. We constructed a Regional Smart Healthcare Service Development Index and matched it with survey data from the China Health and Retirement Longitudinal Study to validate the model. The results showed that (1) smart healthcare services have a significant positive impact on resident health. (2) The availability of outpatient services and inpatient services plays a mediating role in the relationship between regional smart healthcare services and resident health. (3) The influence of regional smart healthcare services on resident health is heterogeneous among different regions. Specifically, the effect of smart healthcare services on resident health is significant in the eastern regions, while it is not significant in the central, western, and northeastern regions. The effect of smart healthcare services on resident health is significant in rural regions but not in urban regions. This study enriches the nascent research stream of smart healthcare services. This study offers useful insights for practitioners and the government to guide them in formulating smart healthcare strategies.
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Verma P, Awasthi VK, Sahu SK, Shrivas AK. Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2022. [DOI: 10.4018/ijamc.292504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Nowadays, many people are suffering from several health related issues in which Coronary Artery Disease (CAD) is an important one. Identification, prevention and diagnosis of diseases is a very challenging task in the field of medical science. This paper proposes a new feature optimization technique known as PSO-Ensemble1 to reduce the number of features from CAD datasets. The proposed model is based on Particle Swarm Optimization (PSO) with Ensemble1 classifier as the objective function and is compared with other optimization techniques like PSO-CFSE and PSO-J48 with two benchmark CAD datasets. The main objective of this research work is to classify CAD with the proposed PSO-Ensemble1 model using the Ensemble Technique.
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Singh D, Prashar D, Singla J, Ahmad Khan A, Al-Sarem M, Ali Kurdi N. Intelligent Medical Diagnostic System for Hepatitis B. COMPUTERS, MATERIALS & CONTINUA 2022; 73:6047-6068. [DOI: 10.32604/cmc.2022.031255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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18
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Iwendi C, Mahboob K, Khalid Z, Javed AR, Rizwan M, Ghosh U. Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system. MULTIMEDIA SYSTEMS 2022; 28:1223-1237. [PMID: 33814730 PMCID: PMC8004563 DOI: 10.1007/s00530-021-00774-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 02/26/2021] [Indexed: 05/05/2023]
Abstract
Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity's body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease's risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.
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Affiliation(s)
- Celestine Iwendi
- Department of Electronics BCC of Central South University of Forestry and Technology, Changsha, China
| | - Kainaat Mahboob
- Department of Computer Science, Kinnaird College for Women University, Lahore, Pakistan
| | - Zarnab Khalid
- Department of Computer Science, Kinnaird College for Women University, Lahore, Pakistan
| | | | - Muhammad Rizwan
- Department of Computer Science, Kinnaird College for Women University, Lahore, Pakistan
| | - Uttam Ghosh
- School of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
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19
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Ahmed SA, Nath B. Identification of adverse disease agents and risk analysis using frequent pattern mining. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.07.061] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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20
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Tan Q, Ye M, Ma AJ, Yip TCF, Wong GLH, Yuen PC. Importance-aware personalized learning for early risk prediction using static and dynamic health data. J Am Med Inform Assoc 2021; 28:713-726. [PMID: 33496786 DOI: 10.1093/jamia/ocaa306] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/17/2020] [Accepted: 11/21/2020] [Indexed: 01/23/2023] Open
Abstract
OBJECTIVE Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity. It is urgent to develop an early risk prediction method that can adaptively integrate both static and dynamic health data. MATERIALS AND METHODS Data were from 6367 patients with Peptic Ulcer Bleeding between 2007 and 2016. This article develops a novel End-to-end Importance-Aware Personalized Deep Learning Approach (eiPDLA) to achieve accurate early clinical risk prediction. Specifically, eiPDLA introduces a long short-term memory with temporal attention to learn sequential dependencies from time-stamped records and simultaneously incorporating a residual network with correlation attention to capture their influencing relationship with static medical data. Furthermore, a new multi-residual multi-scale network with the importance-aware mechanism is designed to adaptively fuse the learned multisource features, automatically assigning larger weights to important features while weakening the influence of less important features. RESULTS Extensive experimental results on a real-world dataset illustrate that our method significantly outperforms the state-of-the-arts for early risk prediction under various settings (eg, achieving an AUC score of 0.944 at 1 year ahead of risk prediction). Case studies indicate that the achieved prediction results are highly interpretable. CONCLUSION These results reflect the importance of combining static and dynamic health data, mining their influencing relationship, and incorporating the importance-aware mechanism to automatically identify important features. The achieved accurate early risk prediction results save precious time for doctors to timely design effective treatments and improve clinical outcomes.
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Affiliation(s)
- Qingxiong Tan
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, Hong Kong
| | - Mang Ye
- School of Computer Science, Wuhan University, Wuhan, China
| | - Andy Jinhua Ma
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Terry Cheuk-Fung Yip
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Grace Lai-Hung Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Pong C Yuen
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, Hong Kong
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Sainin MS, Alfred R, Ahmad F. ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH MULTICLASS IMBALANCE PROBLEM. JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY 2021. [DOI: 10.32890/jict2021.20.2.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Ensemble learning by combining several single classifiers or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach still faces the question of how the ensemble methods obtain their higher performance. In this paper, an investigation was carried out on the design of the meta classifier ensemble with sampling and feature selection for multiclass imbalanced data. The specific objectives were: 1) to improve the ensemble classifier through data-level approach (sampling and feature selection); 2) to perform experiments on sampling, feature selection, and ensemble classifier model; and 3 ) to evaluate t he performance of the ensemble classifier. To fulfil the objectives, a preliminary data collection of Malaysian plants’ leaf images was prepared and experimented, and the results were compared. The ensemble design was also tested with three other high imbalance ratio benchmark data. It was found that the design using sampling, feature selection, and ensemble classifier method via AdaboostM1 with random forest (also an ensemble classifier) provided improved performance throughout the investigation. The result of this study is important to the on-going problem of multiclass imbalance where specific structure and its performance can be improved in terms of processing time and accuracy.
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Affiliation(s)
| | - Rayner Alfred
- Faculty of Computing and Informatics, Universiti Malaysia Sabah, Malaysia
| | - Faudziah Ahmad
- School of Computing, Universiti Utara Malaysia, Malaysia
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22
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Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis. Neural Comput Appl 2020; 33:7649-7660. [PMID: 33250576 PMCID: PMC7684855 DOI: 10.1007/s00521-020-05507-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 11/04/2020] [Indexed: 11/04/2022]
Abstract
Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson’s disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE–GSO–ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE–GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE–GSO–ANFIS over the similar methods in predicting medical disorders.
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23
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Fathi S, Ahmadi M, Birashk B, Dehnad A. Development and use of a clinical decision support system for the diagnosis of social anxiety disorder. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 190:105354. [PMID: 32035305 DOI: 10.1016/j.cmpb.2020.105354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 01/04/2020] [Accepted: 01/21/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND Mental disorders, according to the definition of World Health Organization, consist of a wide range of signs, which are generally specified by a combination of unusual thoughts, feelings, behavior, and relationships with others. Social anxiety disorder (SAD) is one of the most prevalent mental disorders, described as permanent and severe fear or feeling of embarrassment in social situations. Considering the imprecise nature of SAD symptoms, the main objective of this study was to generate an intelligent decision support system for SAD diagnosis, using Adaptive neuro-fuzzy inference system (ANFIS) technique and to conduct an evaluation method, using sensitivity, specificity and accuracy metrics. METHOD In this study, a real-world dataset with the sample size of 214 was selected and used to generate the model. The method comprised a multi-stage procedure named preprocessing, classification, and evaluation. The preprocessing stage, itself, consists of three steps called normalization, feature selection, and anomaly detection, using the Self-Organizing Map (SOM) clustering method. The ANFIS technique with 5-fold cross-validation was used for the classification of social anxiety disorder. RESULTS AND CONCLUSION The preprocessed dataset with seven input features were used to train the ANFIS model. The hybrid optimization learning algorithm and 41 epochs were used as optimal learning parameters. The accuracy, sensitivity, and specificity metrics were reported 98.67%, 97.14%, and 100%, respectively. The results revealed that the proposed model was quite appropriate for SAD diagnosis and in line with findings of other studies. Further research study addressing the design of a decision support system for diagnosing the severity of SAD is recommended.
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Affiliation(s)
- Sina Fathi
- Department of Health Information Management, School of Management and Medical Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Ahmadi
- Department of Health Information Management, School of Management and Medical Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
| | - Behrouz Birashk
- School of Behavioral Science and Mental Health, Iran University of Medical Sciences, Tehran, Iran
| | - Afsaneh Dehnad
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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24
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Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV. ALGORITHMS 2020. [DOI: 10.3390/a13030073] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Hepatitis C virus (HCV) is one of the most dangerous viruses worldwide. It is the foremost cause of the hepatic cirrhosis, and hepatocellular carcinoma, HCC. Detecting new key genes that play a role in the growth of HCC in HCV patients using machine learning techniques paves the way for producing accurate antivirals. In this work, there are two phases: detecting the up/downregulated genes using classical univariate and multivariate feature selection methods, and validating the retrieved list of genes using Insilico classifiers. However, the classification algorithms in the medical domain frequently suffer from a deficiency of training cases. Therefore, a deep neural network approach is proposed here to validate the significance of the retrieved genes in classifying the HCV-infected samples from the disinfected ones. The validation model is based on the artificial generation of new examples from the retrieved genes’ expressions using sparse autoencoders. Subsequently, the generated genes’ expressions data are used to train conventional classifiers. Our results in the first phase yielded a better retrieval of significant genes using Principal Component Analysis (PCA), a multivariate approach. The retrieved list of genes using PCA had a higher number of HCC biomarkers compared to the ones retrieved from the univariate methods. In the second phase, the classification accuracy can reveal the relevance of the extracted key genes in classifying the HCV-infected and disinfected samples.
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25
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Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09804-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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26
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Das H, Naik B, Behera H. Medical disease analysis using Neuro-Fuzzy with Feature Extraction Model for classification. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2019.100288] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Arji G, Ahmadi H, Nilashi M, A Rashid T, Hassan Ahmed O, Aljojo N, Zainol A. Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification. Biocybern Biomed Eng 2019; 39:937-955. [PMID: 32287711 PMCID: PMC7115764 DOI: 10.1016/j.bbe.2019.09.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/15/2019] [Accepted: 09/17/2019] [Indexed: 01/04/2023]
Abstract
This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.
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Affiliation(s)
- Goli Arji
- School of Nursing and Midwifery, Health Information Technology Department, Saveh University of Medical Sciences, Iran
| | - Hossein Ahmadi
- Halal Research Center of IRI, FDA, Tehran, Iran
- Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq
| | - Mehrbakhsh Nilashi
- Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Kurdistan, Iraq
| | - Omed Hassan Ahmed
- School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, United Kingdom
- University of Human Development, College of Science and Technology, Department of Information Technology, Sulaymaniyah, Iraq
| | - Nahla Aljojo
- College of Computer Science and Engineering, Department of Information Systems and Technology, University of Jeddah, Jeddah, Saudi Arabia
| | - Azida Zainol
- Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
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Majeed Alneamy JS, A Hameed Alnaish Z, Mohd Hashim SZ, Hamed Alnaish RA. Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis. Comput Biol Med 2019; 112:103348. [PMID: 31356992 DOI: 10.1016/j.compbiomed.2019.103348] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 06/30/2019] [Accepted: 07/01/2019] [Indexed: 02/07/2023]
Abstract
Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy Wavelet Neural Network (FWNN) with Functional Link Neural Network (FLNN) is proposed. In addition, the TLBO algorithm is utilized for training the new hybrid Functional Fuzzy Wavelet Neural Network (FFWNN) and optimizing the learning parameters, which are weights, dilation and translation. To evaluate the performance of the proposed method, five standard medical datasets were used: Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis. The efficiency of the proposed method is evaluated using 5-fold cross-validation and 10-fold cross-validation in terms of mean square error (MSE), classification accuracy, running time, sensitivity, specificity and kappa. The experimental results show that the efficiency of the proposed method for the medical classification problems is 98.309%, 91.1%, 91.39%, 88.67% and 93.51% for the Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis datasets, respectively, in terms of accuracy after 30 runs for each dataset with low computational complexity. In addition, it has been observed that the proposed method has efficient performance compared with the performance of other methods found in the related previous studies.
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Affiliation(s)
| | | | - S Z Mohd Hashim
- Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.
| | - Rahma A Hamed Alnaish
- Department of Software Engineering, Computer and Mathematics Science College, University of Mosul, Mosul, Iraq.
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Li M, Shang Z, Yang Z, Zhang Y, Wan H. Machine learning methods for MRI biomarkers analysis of pediatric posterior fossa tumors. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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30
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Thukral S, Rana V. Versatility of fuzzy logic in chronic diseases: A review. Med Hypotheses 2018; 122:150-156. [PMID: 30593401 DOI: 10.1016/j.mehy.2018.11.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/15/2018] [Accepted: 11/26/2018] [Indexed: 01/31/2023]
Abstract
The review aims at providing current state of evidence in the field of medicine with fuzzy logic for diagnosing diseases. Literature reveals that fuzzy logic has been used effectively in medicine. Different types of methodologies have been applied to diagnose the diseases based on symptoms, historical and clinical data of an individual. Increase in the number of recent applications of medicine with fuzzy-logic is an indication of growing popularity of fuzzy systems. Fuzzy intelligent systems developed during 2007-2018 have been studied to explore various techniques applied for disease prediction. In the traditional approach, a physician is required to diagnose disease based on historical and clinical data but the intelligent system will help physicians as well as individuals to detect disease at any location of the world. The studies of various fuzzy logic systems and classified fuzzy logic applications in the field of diabetes, iris, heart, breast cancer, dental, cholera, brain tumor, liver, asthma, viral, parkinson, lung, kidney, huntington and chest diseases have been included in the review. This study indicates all the benefits of the fuzzy logic to the society and direction to tackle the diseases that still need software for their accurate detection. Further, different case studies for celiac disease have been reported earlier. The current review aims at exploring the future direction for fuzzy methodologies and domain on celiac disease.
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Affiliation(s)
- Sunny Thukral
- Department of CSA, Sant Baba Bhag Singh University, Jalandhar 144030, India.
| | - Vijay Rana
- Department of CSA, Sant Baba Bhag Singh University, Jalandhar 144030, India
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