Enhancing age-related postural sway classification using partial least squares-discriminant analysis and hybrid feature set

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dc.authoridAlcan, Veysel/0000-0002-7786-8591
dc.contributor.authorAlcan, Veysel
dc.date.accessioned2025-03-17T12:27:37Z
dc.date.available2025-03-17T12:27:37Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractFeature sets in a machine learning algorithm can have an impact on the robustness, interpretability, and characterization of the data. To detect age-related changes, traditional linear methods for analyzing center of pressure (COP) signals offer limited insight into the complex nonlinear dynamics of postural control. To overcome this limitation, a novel approach that combines a partial least squares-discriminant analysis (PLS-DA) classifier with the nonlinear dynamics of COP time series was proposed. Three small feature sets were compared: time-domain features alone, entropy-based features alone, and a hybrid approach incorporating both types of features. The performance of the PLS-DA model was assessed in four different eyes and surface conditions by using the accuracy, sensitivity, selectivity, precision metrics, and ROC curves. The results indicated that the PLS-DA model utilizing the hybrid feature set achieved significantly higher accuracy than the time-domain and entropy-based feature sets. The best classification performance was observed when the eyes were open on a compliant surface, with an overall accuracy of 89% for training and 88% for cross-validation. For the old group, while the results indicated 93% sensitivity, 94% specificity, and 93% precision in the training, the results revealed 88% sensitivity, 93% specificity, and 91% precision in cross-validation. Notably, the hybrid feature set yielded an AUC value of 0.96, indicating a superior performance. This study emphasizes the robust classification capabilities of PLS-DA for age-related postural changes and highlights the effectiveness of utilizing a small hybrid feature set to improve classification accuracy and reliability.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK)
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK). The author didnot receive support from any organization for the submitted work
dc.identifier.doi10.1007/s00521-024-09557-6
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.scopus2-s2.0-85185134397
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s00521-024-09557-6
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2361
dc.identifier.wosWOS:001160440900002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAlcan, Veysel
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectMachine learning
dc.subjectFeature selection
dc.subjectPostural control
dc.subjectAging
dc.subjectPartial least squares-discriminant analysis
dc.titleEnhancing age-related postural sway classification using partial least squares-discriminant analysis and hybrid feature set
dc.typeArticle

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