Neuro-Based Prognosticative Analytics for Parkinson Disease using Random Forest Approach
Srilakshmi Ch1, Kishore M2, Ajay Amarnath R3, Deva Krishnan C4
1Ms. Srilakshmi Ch, Department of Computer Science, Anna University, Chennai, (Tamil Nadu), India.
2Mr. Kishore. M, Student, Department of Computer Science, RMD Engineering College. Chennai, (Tamil Nadu), India.
3Mr. Deva C, Student, Department of Computer Science, RMD Engineering College. Chennai, (Tamil Nadu), India.
4Mr. Ajay Amarnath R, Student, Department of Computer Science, RMD Engineering College. Chennai, (Tamil Nadu), India.
Manuscript received on August 23, 2020. | Revised Manuscript received on September 05, 2020. | Manuscript published on September 10, 2020. | PP: 11-15 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.J74340891020 | DOI: 10.35940/ijitee.J7434.0991120
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Abstract: Parkinson’s malady is the most current neurodegenerative disorder poignant quite ten million folks across the world. There’s no single test at which may be administered for diagnosis Parkinson’s malady. Our aim is to analyze machine learning based mostly techniques for Parkinson malady identification in patients. Our machine learning-based technique is employed to accurately predict the malady by speech and handwriting patterns of humans and by predicting leads to the shape of best accuracy and in addition compare the performance of assorted machine learning algorithms from the given hospital dataset with analysis and classification report and additionally determine the result and prove against with best accuracy and exactness, Recall ,F1 Score specificity and sensitivity.
Keywords: Dataset, Speech, handwriting Machine learning, Classification, Random Forest, Prediction of Accuracy result.
Scope of the Article: Machine learning