An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy
Parkinson’s disease (PD) is a neurological disorder that severely affects motor function, especially gait, requiring accurate diagnosis and assessment instruments. This study presents Dense Multiscale Sample Entropy (DM-SamEn) as an innovative method for diminishing feature dimensions while maintain...
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MDPI AG
2024-12-01
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| author | Minh Tai Pham Nguyen Minh Khue Phan Tran Tadashi Nakano Thi Hong Tran Quoc Duy Nam Nguyen |
| author_facet | Minh Tai Pham Nguyen Minh Khue Phan Tran Tadashi Nakano Thi Hong Tran Quoc Duy Nam Nguyen |
| author_sort | Minh Tai Pham Nguyen |
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| description | Parkinson’s disease (PD) is a neurological disorder that severely affects motor function, especially gait, requiring accurate diagnosis and assessment instruments. This study presents Dense Multiscale Sample Entropy (DM-SamEn) as an innovative method for diminishing feature dimensions while maintaining the uniqueness of signal features. DM-SamEn employs a weighting mechanism that considers the dynamic properties of the signal, thereby reducing redundancy and improving the distinctiveness of features extracted from vertical ground reaction force (VGRF) signals in patients with Parkinson’s disease. Subsequent to the extraction process, correlation-based feature selection (CFS) and sequential backward selection (SBS) refine feature sets, improving algorithmic accuracy. To validate the feature extraction and selection stage, three classifiers—Adaptive Weighted K-Nearest Neighbors (AW-KNN), Radial Basis Function Support Vector Machine (RBF-SVM), and Multilayer Perceptron (MLP)—were employed to evaluate classification efficacy and ascertain optimal performance across selection strategies, including CFS, SBS, and the hybrid SBS-CFS approach. K-fold cross-validation was employed to provide improved evaluation of model performance by assessing the model on various data subsets, thereby mitigating the risk of overfitting and augmenting the robustness of the results. As a result, the model demonstrated a significant ability to differentiate between PD patients and healthy controls, with classification accuracy reported as ACC [CI 95%: 97.82–98.5%] for disease identification and ACC [CI 95%: 96.3–97.3%] for severity assessment. Optimal performance was primarily achieved through feature sets chosen using SBS and the integrated SBS-CFS methods. The findings highlight the model’s potential as an effective instrument for diagnosing PD and assessing its severity, contributing to advancements in clinical management of the condition. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-7ea0764ba5174c9ebeeb7ce4032e5c8d2025-01-24T13:35:05ZengMDPI AGInformation2078-24892024-12-01161110.3390/info16010001An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample EntropyMinh Tai Pham Nguyen0Minh Khue Phan Tran1Tadashi Nakano2Thi Hong Tran3Quoc Duy Nam Nguyen4Faculty of Advanced Program, Ho Chi Minh City Open University, Ho Chi Minh City 700000, VietnamFaculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City 700000, VietnamDepartment of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, JapanDepartment of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, JapanDepartment of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, JapanParkinson’s disease (PD) is a neurological disorder that severely affects motor function, especially gait, requiring accurate diagnosis and assessment instruments. This study presents Dense Multiscale Sample Entropy (DM-SamEn) as an innovative method for diminishing feature dimensions while maintaining the uniqueness of signal features. DM-SamEn employs a weighting mechanism that considers the dynamic properties of the signal, thereby reducing redundancy and improving the distinctiveness of features extracted from vertical ground reaction force (VGRF) signals in patients with Parkinson’s disease. Subsequent to the extraction process, correlation-based feature selection (CFS) and sequential backward selection (SBS) refine feature sets, improving algorithmic accuracy. To validate the feature extraction and selection stage, three classifiers—Adaptive Weighted K-Nearest Neighbors (AW-KNN), Radial Basis Function Support Vector Machine (RBF-SVM), and Multilayer Perceptron (MLP)—were employed to evaluate classification efficacy and ascertain optimal performance across selection strategies, including CFS, SBS, and the hybrid SBS-CFS approach. K-fold cross-validation was employed to provide improved evaluation of model performance by assessing the model on various data subsets, thereby mitigating the risk of overfitting and augmenting the robustness of the results. As a result, the model demonstrated a significant ability to differentiate between PD patients and healthy controls, with classification accuracy reported as ACC [CI 95%: 97.82–98.5%] for disease identification and ACC [CI 95%: 96.3–97.3%] for severity assessment. Optimal performance was primarily achieved through feature sets chosen using SBS and the integrated SBS-CFS methods. The findings highlight the model’s potential as an effective instrument for diagnosing PD and assessing its severity, contributing to advancements in clinical management of the condition.https://www.mdpi.com/2078-2489/16/1/1Parkinson’s Disease detectiondense multiscale sample entropyfeature selection strategyfeature selection processmachine learning |
| spellingShingle | Minh Tai Pham Nguyen Minh Khue Phan Tran Tadashi Nakano Thi Hong Tran Quoc Duy Nam Nguyen An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy Information Parkinson’s Disease detection dense multiscale sample entropy feature selection strategy feature selection process machine learning |
| title | An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy |
| title_full | An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy |
| title_fullStr | An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy |
| title_full_unstemmed | An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy |
| title_short | An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy |
| title_sort | approach for detecting parkinson s disease by integrating optimal feature selection strategies with dense multiscale sample entropy |
| topic | Parkinson’s Disease detection dense multiscale sample entropy feature selection strategy feature selection process machine learning |
| url | https://www.mdpi.com/2078-2489/16/1/1 |
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