PARKINSON’S DISEASE DETECTION THROUGH VOICE SIGNALS

Authors

  • Chittiprolu.Saranya, Chittibomma.Sai Sindhu, Ikkurthi.Chandi Priya, Dantu.Swati, Anne.Dhatri , Alla.Kalavathi Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur

Keywords:

Parkinson’s disease, linear discriminant analysis, similarity measure, fuzzy

Abstract

The primary Parkinson’s disease (PD) is a disorder of the central nervous system and about 89% of the people with PD suffering from speech and voice disorders. In this project, we adopted a dynamic feature selection based on fuzzy entropy measures for speech pattern classification of Parkinson’s diseases. To investigate the effect of feature selection, XGBoost algorithm was applied to distinguish voice samples between PD patients and health people. The data set of this research is composed of voice signals from 195 people, 147 with Parkinson’s disease and 48 healthy people.

 The results show that various voice samples need different feature selection. We applied dynamic feature selection can get higher rate of classification accuracy than all features selected.

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Published

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How to Cite

Chittiprolu.Saranya, Chittibomma.Sai Sindhu, Ikkurthi.Chandi Priya, Dantu.Swati, Anne.Dhatri , Alla.Kalavathi. (2023). PARKINSON’S DISEASE DETECTION THROUGH VOICE SIGNALS. EPRA International Journal of Multidisciplinary Research (IJMR), 9(10), 332–336. Retrieved from http://eprajournals.net/index.php/IJMR/article/view/3056

Issue

Vol. 9 No. 10 (2023): EPRA International Journal of Multidisciplinary Research (IJMR)

Section

Articles