UPDRS tracking using linear regression and neural network for Parkinson’s disease prediction
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Volume & Issue no: Volume 4, Issue 6, November - December 2015

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Title:
UPDRS tracking using linear regression and neural network for Parkinson’s disease prediction
Author Name:
Elmehdi BENMALEK, Jamal ELMHAMDI, Abdelilah JILBAB
Abstract:
Abstract The Unified Parkinsons Disease Rating Scale (UPDRS) is often used to track Parkinsons disease (PD) but it requires costly and logistically inconvenient for patient and clinical staff. In this work we present clinically useful accuracy replication of UPDRS, so we can classify the diseases severity of the patients with, and predict the evolution of PD based on those results. We map the features extracted from the speech to UPDRS using Least-squares regression technique and neural network. We applied our techniques on large database of PD speech (~6,000 recordings from 42PD patients). And we compare our results with state of the art. Keywords: UPDRS tracking, Parkinson disease, linear regression, neural network.
Cite this article:
Elmehdi BENMALEK, Jamal ELMHAMDI, Abdelilah JILBAB , " UPDRS tracking using linear regression and neural network for Parkinson’s disease prediction " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 4, Issue 6, November - December 2015 , pp. 189-193 , ISSN 2278-6856.
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