Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements

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dc.authoridElen, Abdullah/0000-0003-1644-0476
dc.contributor.authorAvuclu, Emre
dc.contributor.authorElen, Abdullah
dc.date.accessioned2024-09-29T15:51:27Z
dc.date.available2024-09-29T15:51:27Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description.abstractParkinson's disease is a neurological disorder that causes partial or complete loss of motor reflexes and speech and affects thinking, behavior, and other vital functions affecting the nervous system. Parkinson's disease causes impaired speech and motor abilities (writing, balance, etc.) in about 90% of patients and is often seen in older people. Some signs (deterioration of vocal cords) in medical voice recordings from Parkinson's patients are used to diagnose this disease. The database used in this study contains biomedical speech voice from 31 people of different age and sex related to this disease. The performance comparison of the machine learning algorithms k-Nearest Neighborhood (k-NN), Random Forest, Naive Bayes, and Support Vector Machine classifiers was performed with the used database. Moreover, the best classifier was determined for the diagnosis of Parkinson's disease. Eleven different training and test data (45 x 55, 50 x 50, 55 x 45, 60 x 40, 65 x 35, 70 x 30, 75 x 25, 80 x 20, 85 x 15, 90 x 10, 95 x 5) were processed separately. The data obtained from these training and tests were compared with statistical measurements. The training results of the k-NN classification algorithm were generally 100% successful. The best test result was obtained from Random Forest classifier with 85.81%. All statistical results and measured values are given in detail in the experimental studies section.en_US
dc.identifier.doi10.1007/s11517-020-02260-3
dc.identifier.endpage2788en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue11en_US
dc.identifier.pmid32920727en_US
dc.identifier.scopus2-s2.0-85090859368en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2775en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-020-02260-3
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4087
dc.identifier.volume58en_US
dc.identifier.wosWOS:000568631000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMedical voice recordingsen_US
dc.subjectMachine learningen_US
dc.subjectParkinson's diseaseen_US
dc.subjectPerformance comparisonen_US
dc.titleEvaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurementsen_US
dc.typeArticleen_US

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