Detection of Parkinson's disease with keystroke data

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dc.authoridvelioglu, yakup sedat/0000-0002-3281-6229
dc.authoridDemircan, Bahar/0000-0002-6983-384X
dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authoriddemir, bahar/0000-0001-7277-1830
dc.authorwosidvelioglu, yakup sedat/A-8730-2012
dc.authorwosidDemircan, Bahar/KDO-2478-2024
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.contributor.authorDemir, Bahar
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorErdemb, Oguzhan
dc.date.accessioned2024-06-12T11:12:05Z
dc.date.available2024-06-12T11:12:05Z
dc.date.issued2023
dc.departmentTrakya Üniversitesien_US
dc.description.abstractParkinson's disease (PD) is one of the most widespread neurological disorders associated with nerve damage without definitive treatment. Impairments, such as trembling and slowing down in hand movements are among the first symptoms. For this purpose, in this study, machine learning (ML)-based models were developed by using keyboard keystroke dynamics. According to patients' drug use status, disease severity, and gender, we created 14 different sub-datasets and extracted 378 features using raw keystroke data. We developed alternative models with Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) algorithms. We further used Minimum Redundancy Maximum Relevance (mRmR), RELIEF, sequential forward selection (SFS), and RF feature selection methods to investigate prominent features in distinguishing PD. We developed ML models that jointly use the most popular features of selection algorithms (feature ensemble [FE]) and an ensemble classifier by combining multiple ML algorithms utilizing majority vote (model ensemble [ME]). With 14 different sets, FE and ME models provided accuracy (Acc.) in the range of 91.73 - 100% and 81.08 - 100%, respectively.en_US
dc.identifier.doi10.1080/10255842.2023.2245516
dc.identifier.endpage1667en_US
dc.identifier.issn1025-5842
dc.identifier.issn1476-8259
dc.identifier.issue13en_US
dc.identifier.pmid38117069en_US
dc.identifier.scopus2-s2.0-85168376088en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1653en_US
dc.identifier.urihttps://doi.org/10.1080/10255842.2023.2245516
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23041
dc.identifier.volume26en_US
dc.identifier.wosWOS:001168542300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofComputer Methods In Biomechanics And Biomedical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParkinsonen_US
dc.subjectKeystrokeen_US
dc.subjectMachine Learning (ML)en_US
dc.subjectHand-Finger Movement Analysisen_US
dc.subjectFeature Ensemble (FE)en_US
dc.subjectLifeen_US
dc.titleDetection of Parkinson's disease with keystroke dataen_US
dc.typeArticleen_US

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