Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor
Neharika D Bala1, Anusuya S2
1Neharika D Bala, Student, Information Technology, Saveetha Institue of Medical and Technical Sciences, Saveetha University, Chennai, India.
2Anusuya S, Professor and Head, Information Technology, Saveetha Institue of Medical and Technical Sciences, Saveetha University, Chennai, India.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 30, 2020. | Manuscript published on March 30, 2020. | PP: 47-50 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7129038620/2020©BEIESP | DOI: 10.35940/ijrte.F7129.038620
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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: Generally, the diseases are classified into communicable and non-communicable. The communicable disease is that, which can be spread easily from humans to humans while non-communicable disease does not spread. In this paper, we discuss about Parkinson’s disease and its analysis using machine learning algorithms. The analysis of data is done using supervised machine learning approach. This paper concentrates and briefs about various supervised learning algorithms and its analysis.
Keywords: Prediction Rate, Death Rate, Support Vector Machine, Data Analysis.
Scope of the Article: Machine Learning