By Topic

Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Yadav, G. ; Dept. of Pharm. Sci., Birla Inst. of Technol., Ranchi, India ; Kumar, Y. ; Sahoo, G.

The prediction of Parkinson's disease in early age has been challenging task among researchers because the symptoms of disease come into existence in middle and late middle age. There is lot of the symptoms that leads to Parkinson's disease. But this paper focus 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. So, the main task of this paper is tried to find out which model identified the PD affected people more accurately.

Published in:

Computing and Communication Systems (NCCCS), 2012 National Conference on

Date of Conference:

21-22 Nov. 2012