Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine Learning
The diagnosis of Parkinson's disease (PD) is usually done clinically by a doctor. This diagnosis is based on the initial symptoms, motor symptoms, and meditation of the doctor's experience. Since the diagnosis is made with the help of a doctor and based on the clinical description and rece...
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University of Isfahan
2024-09-01
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| Series: | هوش محاسباتی در مهندسی برق |
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| Online Access: | https://isee.ui.ac.ir/article_28351_677d88b42661d4e5c0f917dab3e9071a.pdf |
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| author | Shamimeh Sadat Nabavi Monfared Mohammad Reza Yousefi |
| author_facet | Shamimeh Sadat Nabavi Monfared Mohammad Reza Yousefi |
| author_sort | Shamimeh Sadat Nabavi Monfared |
| collection | DOAJ |
| description | The diagnosis of Parkinson's disease (PD) is usually done clinically by a doctor. This diagnosis is based on the initial symptoms, motor symptoms, and meditation of the doctor's experience. Since the diagnosis is made with the help of a doctor and based on the clinical description and received information, there is always an error in the diagnosis. Also, early clinical diagnosis is very difficult and almost impossible. Using methods based on machine learning is very useful for early diagnosis of Parkinson's disease. Brain signals and brain function can be a suitable solution for early diagnosis of this disease. Conventional methods are not effective due to the dynamics and complexity of the brain signal. Machine learning methods are a suitable solution with their high capabilities in the process of disease diagnosis. In this article, an efficient method based on machine learning is presented. In this method, after brain signals are pre-processed, time and frequency domain features are extracted from each signal and the best features are selected with the help of the improved intelligent gray wolf algorithm. The selected features are classified using a support vector machine classifier, K nearest neighbor, and random forest. Accuracy higher than 97% shows the superiority of the method in predicting Parkinson's disease. |
| format | Article |
| id | doaj-art-b71dbb7d0a544994bb2af08c0fc768d3 |
| institution | Kabale University |
| issn | 2821-0689 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | University of Isfahan |
| record_format | Article |
| series | هوش محاسباتی در مهندسی برق |
| spelling | doaj-art-b71dbb7d0a544994bb2af08c0fc768d32025-01-26T07:58:38ZengUniversity of Isfahanهوش محاسباتی در مهندسی برق2821-06892024-09-0115313715010.22108/isee.2024.139761.166528351Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine LearningShamimeh Sadat Nabavi Monfared0Mohammad Reza Yousefi1M.Sc., Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, IranAssistant Professor, Department of Electrical Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, IranThe diagnosis of Parkinson's disease (PD) is usually done clinically by a doctor. This diagnosis is based on the initial symptoms, motor symptoms, and meditation of the doctor's experience. Since the diagnosis is made with the help of a doctor and based on the clinical description and received information, there is always an error in the diagnosis. Also, early clinical diagnosis is very difficult and almost impossible. Using methods based on machine learning is very useful for early diagnosis of Parkinson's disease. Brain signals and brain function can be a suitable solution for early diagnosis of this disease. Conventional methods are not effective due to the dynamics and complexity of the brain signal. Machine learning methods are a suitable solution with their high capabilities in the process of disease diagnosis. In this article, an efficient method based on machine learning is presented. In this method, after brain signals are pre-processed, time and frequency domain features are extracted from each signal and the best features are selected with the help of the improved intelligent gray wolf algorithm. The selected features are classified using a support vector machine classifier, K nearest neighbor, and random forest. Accuracy higher than 97% shows the superiority of the method in predicting Parkinson's disease.https://isee.ui.ac.ir/article_28351_677d88b42661d4e5c0f917dab3e9071a.pdfparkinson's diseasebrain signalmachine learningfeature selectionintelligent gray wolf algorithm |
| spellingShingle | Shamimeh Sadat Nabavi Monfared Mohammad Reza Yousefi Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine Learning هوش محاسباتی در مهندسی برق parkinson's disease brain signal machine learning feature selection intelligent gray wolf algorithm |
| title | Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine Learning |
| title_full | Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine Learning |
| title_fullStr | Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine Learning |
| title_full_unstemmed | Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine Learning |
| title_short | Improving the Accuracy of Early Detection of Parkinson's Disease Using Brain Signals Based on Feature Selection in Machine Learning |
| title_sort | improving the accuracy of early detection of parkinson s disease using brain signals based on feature selection in machine learning |
| topic | parkinson's disease brain signal machine learning feature selection intelligent gray wolf algorithm |
| url | https://isee.ui.ac.ir/article_28351_677d88b42661d4e5c0f917dab3e9071a.pdf |
| work_keys_str_mv | AT shamimehsadatnabavimonfared improvingtheaccuracyofearlydetectionofparkinsonsdiseaseusingbrainsignalsbasedonfeatureselectioninmachinelearning AT mohammadrezayousefi improvingtheaccuracyofearlydetectionofparkinsonsdiseaseusingbrainsignalsbasedonfeatureselectioninmachinelearning |