Heart Disease Prediction Based on Age Detection using Novel Logistic Regression over Support Vector Machine

Abstract

Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and Support Vector Machine. Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and Support Vector Machine. Each group consists of a sample size of 10 and the study parameters include alpha value 0.01, beta value 0.2, and the Gpower value of 0.8. Results: The Novel Logistic Regression (91.60) achieved improved accuracy than the Support Vector Machine (91.83) in Heart Disease Prediction. The statistical significance difference (two-tailed) is 0.01 (p<0.05). Conclusion: The Novel Logistic Regression model is significantly better than the Support Vector Machine in Heart Disease Prediction. It can be also considered a better option for Heart Disease Prediction.