Generation of a fuzzy classifier rule base for diagnosing Parkinson's disease from handwritten data

Parkinson's disease is a neurodegenerative neurological disease which progression can be slowed by accurate and timely diagnosis. In this connection, the development of simple and accessible screening methods is relevant, one of which is the analysis of handwriting and drawing. The paper describes such a method based on the application of fuzzy classifier. The algorithm for formation of fuzzy rules bases, in which mountain clustering is applied after a parameters’ adjustment on concrete data, is offered. The Powell's optimization algorithm is chosen to find parameters. The balanced accuracy and the ratio of the number of rules to the number of training samples is used as the target function. The effectiveness of the proposed algorithm is compared with the classical k-means clustering algorithm and the extreme class feature algorithm.