{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:38:27Z","timestamp":1769751507974,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency (JST) under Strategic Basic Research Programs Precursory Research for Embryonic Science and Technology (PRESTO)","doi-asserted-by":"publisher","award":["JPMJPR20M6"],"award-info":[{"award-number":["JPMJPR20M6"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Parkinson\u2019s 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\u2019s 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\u2014Adaptive Weighted K-Nearest Neighbors (AW-KNN), Radial Basis Function Support Vector Machine (RBF-SVM), and Multilayer Perceptron (MLP)\u2014were 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\u201398.5%] for disease identification and ACC [CI 95%: 96.3\u201397.3%] 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\u2019s potential as an effective instrument for diagnosing PD and assessing its severity, contributing to advancements in clinical management of the condition.<\/jats:p>","DOI":"10.3390\/info16010001","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T10:58:32Z","timestamp":1735037912000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Approach for Detecting Parkinson\u2019s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7329-6262","authenticated-orcid":false,"given":"Minh Tai Pham","family":"Nguyen","sequence":"first","affiliation":[{"name":"Faculty of Advanced Program, Ho Chi Minh City Open University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7711-9586","authenticated-orcid":false,"given":"Minh Khue Phan","family":"Tran","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tadashi","family":"Nakano","sequence":"additional","affiliation":[{"name":"Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thi Hong","family":"Tran","sequence":"additional","affiliation":[{"name":"Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quoc Duy Nam","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Osaka 558-8585, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.1038\/s41591-023-02440-2","article-title":"Wearable movement-tracking data identify Parkinson\u2019s disease years before clinical diagnosis","volume":"29","author":"Schalkamp","year":"2023","journal-title":"Nat. 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