Fuzzy Classifiers for Diagnosing of Parkinson’s Disease Based on Static Handwritten Data

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. Handwritten static drawings of spirals and meanders are analyzed using machine learning for diagnosing of Parkinson’s disease based on the publicly available HandPD dataset. Fuzzy classifiers make it possible to identify a disease by a pattern are constructed using original methods. Since the HandPD dataset is imbalanced, artificial sampling algorithms are used in the work. The accuracy of the used models and methods is statistically compared; features are ranked.