title: Predication of Parkinson's disease using data mining methods: a comparative analysis of tree, statistical, and support vector machine classifiers.
abstract: The prediction of Parkinson's disease in early age has been challenging task among researchers, because the symptoms of disease came into existence in middle and late middle age. There are lots of symptoms that lead to Parkinson's disease. But this article focuses on the speech articulation difficulty symptoms of PD affected people and try to formulate the model on the behalf of three data mining methods. These three data mining methods are taken from three different domains of data mining i.e., from tree classifier, statistical classifier, and support vector machine classifier. Performance of these three classifiers is measured with three performance matrices i.e., accuracy, sensitivity, and specificity. Hence, the main task of this article is tried to find out which model identified the PD affected people more accurately.
authors: Department of Pharmaceutical Sciences, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
authors: Geeta
authors: G
authors: Yadav
authors: Yugal
authors: Y
authors: Kumar
authors: Gadadhar
authors: G
authors: Sahoo
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010300:Parkinson Disease / Q000145:classification*; D060388:Support Vector Machine