Volume - 13 | Issue-1
Volume - 13 | Issue-1
Volume - 13 | Issue-1
Volume - 13 | Issue-1
Volume - 13 | Issue-1
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.