Journal
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Volume 82, Issue 5, Pages 359-377Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2012.10.006
Keywords
Parkinson's disease corrected T-tests; Multinomial logistic regression classifier; Haar wavelets transformation; Inference system; Support Vector Machines; Ranker search; Artificial Neural Networks; Boosting; Statistical inference
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This work presents more precise computational methods for improving the diagnosis of Parkinson's disease based on the detection of dysphonia. New methods are presented for enhanced evaluation and recognize Parkinson's disease affected patients at early stage. Analysis is performed with significant level of error tolerance rate and established our results with corrected T-test. Here new ensembles and other machine learning methods consisting of multinomial logistic regression classifier with Haar wavelets transformation as projection filter that outperform logistic regression is used. Finally a novel and reliable inference system is presented for early recognition of people affected by this disease and presents a new measure of the severity of the disease. Feature selection method is based on Support Vector Machines and ranker search method. Performance analysis of each model is compared to the existing methods and examines the main advancements and concludes with propitious results. Reliable methods are proposed for treating Parkinson's disease that includes sparse multinomial logistic regression, Bayesian network, Support Vector Machines, Artificial Neural Networks, Boosting methods and their ensembles. The study aim at improving the quality of Parkinson's disease treatment by tracking them and reinforce the viability of cost effective, regular and precise telemonitoring application. (C) 2012 Elsevier Ireland Ltd. All rights reserved.
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