A new hybrid approach based on AOA, CNN and feature fusion that can automatically diagnose Parkinson's disease from sound signals: PDD-AOA-CNN

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dc.contributor.authorYildirim, Muhammed
dc.contributor.authorKiziloluk, Soner
dc.contributor.authorAslan, Serpil
dc.contributor.authorSert, Eser
dc.date.accessioned2026-06-19T06:41:03Z
dc.date.available2026-06-19T06:41:03Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractParkinson's is one of the most rapidly increasing neurological diseases in the world, caused by the deficiency of dopamine-producing cells in the brain. Voice disorders are a significant finding in the early stage of Parkinson's disease (PD). Detection of this finding at an early stage of the disease allows early treatment of the disease. Therefore, in this study, using sound data, a hybrid model for detecting PD has been designed. In the developed method, first of all, the sound data were converted into spectrograms. Then, the feature maps of the obtained spectrogram images were extracted using 3 different CNN architectures. Feature maps with different features obtained by utilizing the accumulation of different architectures were combined. Then, these features were selected using the arithmetic optimization algorithm (AOA), one of the most recent metaheuristic optimization algorithms, and then classified by support vector machine (SVM) and K-nearest neighbors (KNN). One of the important novelties in the study is the reduction of the size of the acquired feature maps with AOA, a new and high-performance metaheuristic approach. The success of the proposed model in diagnosing Parkinson's disease reached up to 98.19%. In addition, feature maps of the sound data in the dataset were acquired by using the MFCC method to compare the performance of the proposed model. Eight different classifiers were used to categorize the acquired feature maps. The highest accuracy value obtained in this method was obtained in the Random Forest classifier with 93.98%.
dc.identifier.doi10.1007/s11760-023-02826-2
dc.identifier.endpage1240
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue2
dc.identifier.orcid0000-0002-0381-9631
dc.identifier.orcid0000-0002-8611-701X
dc.identifier.scopus2-s2.0-85175378807
dc.identifier.scopusqualityQ2
dc.identifier.startpage1227
dc.identifier.urihttps://doi.org/10.1007/s11760-023-02826-2
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6032
dc.identifier.volume18
dc.identifier.wosWOS:001091795900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectParkinson Sounds
dc.subjectCnn
dc.subjectNca
dc.subjectClassifiers
dc.subjectDeep Learning
dc.titleA new hybrid approach based on AOA, CNN and feature fusion that can automatically diagnose Parkinson's disease from sound signals: PDD-AOA-CNN
dc.typeArticle

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