A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease
Parkinson's disease (PD) is a progressive neurodegenerative motor system disorder. Early diagnosis of PD is important to control the symptoms appropriately. Recent voice and speech recognition techniques provide alternative solutions for PD screening. In this paper, an optimal support vector ma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8016562/ |
| _version_ | 1818370383741452288 |
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| author | Zhennao Cai Jianhua Gu Hui-Ling Chen |
| author_facet | Zhennao Cai Jianhua Gu Hui-Ling Chen |
| author_sort | Zhennao Cai |
| collection | DOAJ |
| description | Parkinson's disease (PD) is a progressive neurodegenerative motor system disorder. Early diagnosis of PD is important to control the symptoms appropriately. Recent voice and speech recognition techniques provide alternative solutions for PD screening. In this paper, an optimal support vector machine (SVM) based on bacterial foraging optimization (BFO) was established to predict PD effectively. The effectiveness of the proposed method, BFO-SVM, was validated on a PD data set based on vocal measurements. The proposed method was compared with two of the most frequently used parameter optimization methods, including an SVM based on the grid search method and an SVM based on particle swarm optimization. Additionally, to further boost the prediction accuracy, the relief feature selection was employed prior to the BFO-SVM method, consequently the RF-BFO-SVM was proposed. The experimental results have demonstrated that the proposed framework exhibited excellent classification performance with a superior classification accuracy of 97.42%. |
| first_indexed | 2024-12-13T23:38:52Z |
| format | Article |
| id | doaj.art-82f289c7021e4fde80537000b740a7ea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:38:52Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-82f289c7021e4fde80537000b740a7ea2022-12-21T23:27:13ZengIEEEIEEE Access2169-35362017-01-015171881720010.1109/ACCESS.2017.27415218016562A New Hybrid Intelligent Framework for Predicting Parkinson’s DiseaseZhennao Cai0Jianhua Gu1Hui-Ling Chen2https://orcid.org/0000-0002-7714-9693School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, ChinaSchool of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, ChinaCollege of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, ChinaParkinson's disease (PD) is a progressive neurodegenerative motor system disorder. Early diagnosis of PD is important to control the symptoms appropriately. Recent voice and speech recognition techniques provide alternative solutions for PD screening. In this paper, an optimal support vector machine (SVM) based on bacterial foraging optimization (BFO) was established to predict PD effectively. The effectiveness of the proposed method, BFO-SVM, was validated on a PD data set based on vocal measurements. The proposed method was compared with two of the most frequently used parameter optimization methods, including an SVM based on the grid search method and an SVM based on particle swarm optimization. Additionally, to further boost the prediction accuracy, the relief feature selection was employed prior to the BFO-SVM method, consequently the RF-BFO-SVM was proposed. The experimental results have demonstrated that the proposed framework exhibited excellent classification performance with a superior classification accuracy of 97.42%.https://ieeexplore.ieee.org/document/8016562/Bacterial foraging optimizationdisease diagnosismedical diagnosisparameter optimizationparkinson’s disease diagnosissupport vector machines |
| spellingShingle | Zhennao Cai Jianhua Gu Hui-Ling Chen A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease IEEE Access Bacterial foraging optimization disease diagnosis medical diagnosis parameter optimization parkinson’s disease diagnosis support vector machines |
| title | A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease |
| title_full | A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease |
| title_fullStr | A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease |
| title_full_unstemmed | A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease |
| title_short | A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease |
| title_sort | new hybrid intelligent framework for predicting parkinson x2019 s disease |
| topic | Bacterial foraging optimization disease diagnosis medical diagnosis parameter optimization parkinson’s disease diagnosis support vector machines |
| url | https://ieeexplore.ieee.org/document/8016562/ |
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