Application of AI for Analysis of Parkinson’s Disease 
Harsh Pandey1, Arjun Shivnani2, Aryaman Chauhan3, Aditya Pratap Singh4, Pauras Khadakban5
1Harsh Pandey*, IT Professional, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
2Arjun Shivnani, IT Professional, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
3Aryaman Chauhan, IT Professional, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
4Aditya Pratap Singh, IT Professional, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
5Pauras Khadakban, IT Professional, Department of Information Technology, Manipal University Jaipur (Rajasthan), India.
Manuscript received on August 03, 2021. | Revised Manuscript received on September 09, 2021. | Manuscript published on September 30, 2021. | PP: 33-39 | Volume-11, Issue-1, September 2021. | Retrieval Number: 100.1/ijsce.A35270911121 | DOI: 10.35940/ijsce.A3527.0911121
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© The Authors. Published By: 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 an issue of the central tactile framework that impacts advancement provoking shudders. The tangible cell is hurt in the frontal cortex causing dopamine levels to drop which prompts the condition. Parkinson’s is a reformist ailment that causes degeneration of the frontal cortex, provoking both motor and mental issues. While Dysphonia is a voice issue that causes mandatory fits in the larynx muscle, this is one of its indications. While, Bradykinesia, which is ordinarily described as slowness of improvements, is one of the cardinal signs of Parkinson’s sickness (PD). Essential clinical rating scales are used usually to measure bradykinesia in routine clinical practice albeit this kind of examination is uneven. It requires clinical investigation, and it can happen starting from the age of 6. Along these lines, this is a starter study that endeavors to recognize connections between Parkinson’s contamination factors for basic unmistakable verification of the sickness. There are 1 million cases in India. It is hence reasonable to acknowledge that there is a connection between a patient’s ability to talk/make and the development towards Parkinson’s as these limits rot as time propels. The mark of the examination was to survey the features of the sound data and the hour of contorting drawing as an extent of bradykinesia. Henceforth to make strong proof that vocalization data and the handwriting test from a patient can assist with dissecting whether they experience the evil impacts of Parkinson’s. As needs be, it is at first anticipated that there is an association between the two. We attempt to run distinctive AI classifiers on the data in wants to show up at a high consistency rate that is facilitated with a reasonable runtime. The dataset managed is procured from a new report by the journal, IEEE Transactions on Biomedical Engineering, of various limits of voice repeat. The actual assessment obtained a consistency speed of 95.58% hence we want to show up at a rate close to this or possibly to beat it.
Keywords: Parkinson’s Disease, Machine Learning, Logistic Regression, Extremely Randomized Trees Classifier