A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity
Aims: The aim of this study was to establish a model for prediction and early diagnosis of multiple sclerosis (MS) based on motion-dependent neurophysiological variables. Main methods: The statistical population included 110 volunteers with and without MS in Mazandaran province, Iran. Based on the i...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Medicine in Drug Discovery |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590098622000136 |
| _version_ | 1798037061106663424 |
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| author | Vahid Talebi Ziya Fallah Mohammadi Sayed Esmaeil Hosseininejad Hossein Falah Mohammadi |
| author_facet | Vahid Talebi Ziya Fallah Mohammadi Sayed Esmaeil Hosseininejad Hossein Falah Mohammadi |
| author_sort | Vahid Talebi |
| collection | DOAJ |
| description | Aims: The aim of this study was to establish a model for prediction and early diagnosis of multiple sclerosis (MS) based on motion-dependent neurophysiological variables. Main methods: The statistical population included 110 volunteers with and without MS in Mazandaran province, Iran. Based on the information provided by the subjects, they were assigned into the following groups; MS and control groups, and based on disease model they were further divided into relapsing-remitting (RR), progressive-relapsing (PR) and control groups, and according to the activity levels they were assigned into active MS, sedentary MS, active control and sedentary control groups. The Support Vector Machine (SVM) method was used to ensure separation and prediction accuracy. All calculations were performed using MATLAB software (version 2016). Key findings: 99.1% separation accuracy and 90% prediction accuracy were observed in non-kinematic data, while in kinematic and electromyography (EMG) data, this was 66% for separation accuracy and 65% regarding prediction accuracy. Among the measured variables, static balance and strength had the greatest effect on prediction results. Significance: Using SVM technique and incorporating early symptoms of MS, we were able to achieve a high precision in predicting MS among the participants. Based on SVM, we achieved a considerably higher prediction accuracy extrapolated from non-kinematic dataset compared to kinematic and EMG datasets. Therefore, this study has opened up a great avenue towards predicting MS based on clinical parameters which could provide the clinicians with information regarding progression of the disease well in advance helping in opting for the best treatment strategies. |
| first_indexed | 2024-04-11T21:21:06Z |
| format | Article |
| id | doaj.art-ad2bf504b5bb458fa4b949a1f3c0f7a2 |
| institution | Directory Open Access Journal |
| issn | 2590-0986 |
| language | English |
| last_indexed | 2024-04-11T21:21:06Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Medicine in Drug Discovery |
| spelling | doaj.art-ad2bf504b5bb458fa4b949a1f3c0f7a22022-12-22T04:02:37ZengElsevierMedicine in Drug Discovery2590-09862022-09-0115100132A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activityVahid Talebi0Ziya Fallah Mohammadi1Sayed Esmaeil Hosseininejad2Hossein Falah Mohammadi3Department of Exercise Physiology, Faculty of Sport Sciences, University of Mazandaran, Babolsar, IranDepartment of Exercise Physiology, Faculty of Sport Sciences, University of Mazandaran, Babolsar, Iran; Corresponding author at: Pasdaran St. Babolsar, 4741613534 Mazandaran, Iran.Department of Sport Biomechanics and Motor Behavior, Faculty of Sport Sciences, University of Mazandaran, Babolsar, IranInstitute of Biochemistry and Molecular Biology, Ulm University, 89081 Ulm, GermanyAims: The aim of this study was to establish a model for prediction and early diagnosis of multiple sclerosis (MS) based on motion-dependent neurophysiological variables. Main methods: The statistical population included 110 volunteers with and without MS in Mazandaran province, Iran. Based on the information provided by the subjects, they were assigned into the following groups; MS and control groups, and based on disease model they were further divided into relapsing-remitting (RR), progressive-relapsing (PR) and control groups, and according to the activity levels they were assigned into active MS, sedentary MS, active control and sedentary control groups. The Support Vector Machine (SVM) method was used to ensure separation and prediction accuracy. All calculations were performed using MATLAB software (version 2016). Key findings: 99.1% separation accuracy and 90% prediction accuracy were observed in non-kinematic data, while in kinematic and electromyography (EMG) data, this was 66% for separation accuracy and 65% regarding prediction accuracy. Among the measured variables, static balance and strength had the greatest effect on prediction results. Significance: Using SVM technique and incorporating early symptoms of MS, we were able to achieve a high precision in predicting MS among the participants. Based on SVM, we achieved a considerably higher prediction accuracy extrapolated from non-kinematic dataset compared to kinematic and EMG datasets. Therefore, this study has opened up a great avenue towards predicting MS based on clinical parameters which could provide the clinicians with information regarding progression of the disease well in advance helping in opting for the best treatment strategies.http://www.sciencedirect.com/science/article/pii/S2590098622000136Disease predictionMultiple Sclerosis (MS)Machine learningSupport Vector Machine (SVM) |
| spellingShingle | Vahid Talebi Ziya Fallah Mohammadi Sayed Esmaeil Hosseininejad Hossein Falah Mohammadi A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity Medicine in Drug Discovery Disease prediction Multiple Sclerosis (MS) Machine learning Support Vector Machine (SVM) |
| title | A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity |
| title_full | A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity |
| title_fullStr | A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity |
| title_full_unstemmed | A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity |
| title_short | A machine learning-based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity |
| title_sort | machine learning based model to evaluate multiple sclerosis predictor factors with emphasis on neurophysiological indices of physical activity |
| topic | Disease prediction Multiple Sclerosis (MS) Machine learning Support Vector Machine (SVM) |
| url | http://www.sciencedirect.com/science/article/pii/S2590098622000136 |
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