Layer recurrent neural network-based diagnosis of Parkinson's disease using voice features

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dc.contributor.authorŞentürk, Zehra Karapınar
dc.date.accessioned2023-07-26T11:50:15Z
dc.date.available2023-07-26T11:50:15Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractParkinson's disease (PD), a slow-progressing neurological disease, affects a large percentage of the world's elderly population, and this population is expected to grow over the next decade. As a result, early detection is crucial for community health and the future of the globe in order to take proper safeguards and have a less arduous treatment procedure. Recent research has begun to focus on the motor system deficits caused by PD. Because practically most of the PD patients suffer from voice abnormalities, researchers working on automated diagnostic systems investigate vocal impairments. In this paper, we undertake extensive experiments with features extracted from voice signals. We propose a layer Recurrent Neural Network (RNN) based diagnosis for PD. To prove the efficiency of the model, different network models are compared. To the best of our knowledge, several neural network topologies, namely RNN, Cascade Forward Neural Networks (CFNN), and Feed Forward Neural Networks (FFNN), are used and compared for voice-based PD detection for the first time. In addition, the impacts of data normalization and feature selection (FS) are thoroughly examined. The findings reveal that normalization increases classifier performance and Laplacian-based FS outperforms. The proposed RNN model with 300 voice features achieves 99.74% accuracy.en_US
dc.identifier.doi10.1515/bmt-2022-0022
dc.identifier.endpage266en_US
dc.identifier.issn0013-5585
dc.identifier.issn1862-278X
dc.identifier.issue4en_US
dc.identifier.pmid35659859en_US
dc.identifier.scopus2-s2.0-85131936356en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage249en_US
dc.identifier.urihttps://doi.org/10.1515/bmt-2022-0022
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12287
dc.identifier.volume67en_US
dc.identifier.wosWOS:000805856000001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞentürk, Zehra Karapınar
dc.language.isoenen_US
dc.publisherWalter De Gruyter Gmbhen_US
dc.relation.ispartofBiomedical Engineering-Biomedizinische Techniken_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectEarly Diagnosis; Machine Learning; Neural Networks; Parkinson's Disease; Rnnen_US
dc.subjectClassification; Relevanceen_US
dc.titleLayer recurrent neural network-based diagnosis of Parkinson's disease using voice featuresen_US
dc.typeArticleen_US

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