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ISSN 2063-5346
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ENHANCING THE ACCURACY IN PARKINSON’S DISEASE PREDICTION USING LOGISTIC REGRESSION COMPARED WITH SUPPORT VECTOR MACHINE

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M. Evelyn Joyce Jebaselvi, K. Somasundaram
» doi: 10.31838/ecb/2023.12.sa1.364

Abstract

Aim: The purpose of this work is to identify whether the person is affected by Parkinson’s Disease or not and give results as a prediction. Materials and Methods: The performance analysis for maximum accuracy in prediction of Parkinson’s Disease using Logistic Regression over Support Vector Machine (SVM) which identifies and predicts the disease. Each group consists of a sample size of 10 and the study parameters include alpha value 0.05, beta value 0.2 and the power value 0.8. Results: The Logistic Regression of 93.95% is more accurate than the Support Vector Machine of 90.72% in prediction of Parkinson's Disease. Conclusion: The Logistic Regression (93%) model is significantly better than the Support Vector Machine (90%) in predicting Parkinson's disease. It can also be considered as a better option for the prediction of Parkinson's disease.

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