Diagnosis of Parkinson’s Disorder through Speech Data using Machine Learning Algorithms
Abhishek M. S1, Chethan C. R2, Aditya C. R3, Divitha D4, Nagaraju T. R5
1Abhishek M. S.*, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India.
2Chethan C. R, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India.
3Aditya C. R., Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India.
4Divitha D, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India.
5Nagaraju T. R, Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysore, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 69-72 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8060019320/2020©BEIESP | DOI: 10.35940/ijitee.C8060.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Parkinson’s disease is a neurodegenerative disorder that affects millions of people around the globe. Detecting Parkinson’s disease at an earlier stage could help to better diagnose the disease. Machine learning provides potentially large opportunities for computer-aided identification and diagnosis that could minimize unavoidable health care errors and inherent clinical uncertainty, provide guidance, and improve decision-making. In this paper, we explore the feature extraction and prediction algorithms used to predict Parkinson’s disease and provide a comprehensive comparison of these algorithms.
Keywords: Parkinson’s disease, PD, Datasets, SVM, KNN, Genetic Algorithm, UPDRS.
Scope of the Article: Algorithm Engineering