Comparison of classification methods to detect the Parkinson disease

Abstract

Parkinson's disease is a chronic neurological degenerative disease affecting the central nervous system responsible for essentially progressive evolution movement disorders. The detection of this disease is made using a clinical diagnosis made by an expert. To save time and for more comfort in the Diagnostic and also to increase the efficiency of treatment through preventive detection, classification has found its place in the detection of this disease. For this purpose, many classification algorithms were used to achieve the best results, but the problem is which classifiers may be the most efficient for this detection, because each classification algorithms was applied to a local database, what influences the results. In this paper we have tried to apply three types of classifiers, k-near neighbor "k-NN", the Naive Bayes "NB" and support vector machines "SVM", on the same database to compare and to know which of the three classifiers will be the most efficient.