FUZZY CLASSIFIERS FOR PARKINSON`S DISEASE DIAGNOSIS BASED ON STATIC HANDWRITTEN DATA
I.A. Hodashinsky, Y.A. Shurygin, K.S. Sarin, M.B. Bardamova, A.O. Slezkin, M.O. Svetlakov, N.P. Koryshev
Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russia
Keywords: neurodegenerative diseases, machine learning, classification, feature selection, fuzzy classifiers, metaheuristic algorithms
Abstract
Diagnosis of Parkinson's disease is an expensive procedure that includes transcranial sonography and brain tomography. In this regard, simple and accurate screening diagnostic methods are relevant. The article deals with the analysis of handwritten static drawings of spirals and meanders using machine learning methods for diagnosing Parkinson's disease based on the publicly available HandPD dataset. Fuzzy classifiers are constructed using original methods that are able to determine the presence or absence of the disease by drawing. As the Hand PD dataset is unbalanced, oversampling algorithms are used in the work. A statistical comparison of the accuracy of the applied models and methods is carried out. The ranking of features is performed.
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