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

Content Provider	IEEE Xplore Digital Library
Author	Yadav, G. Kumar, Y. Sahoo, G.
Copyright Year	2012
Description	Author affiliation: Dept. of Pharmaceutical Sciences, Birla, Institute of Technology, Mesra, Ranchi, Jhrakhand, India (Yadav, G.) \|\| Dept. of Information Technology, Birla, Institute of Technology, Mesra, Ranchi, Jhrakhand, India (Kumar, Y.) \|\| Dept. of Information, Technology, Birla Institute of Technology, Mesra, Ranchi, Jhrakhand, India (Sahoo, G.)
Abstract	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.
Starting Page	1
Ending Page	8
File Size	756231
Page Count	8
File Format	PDF
ISBN	9781467319522
e-ISBN	9781467319539
DOI	10.1109/NCCCS.2012.6413034
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-21
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Classifiers Accuracy Parkinson's disease Logistic Regression Frequency measurement Biomedical measurements Parkinson's and Sequential minimization optimization Machine Learning Decision Stump Logistics
Content Type	Text
Resource Type	Article

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