Heart disease prediction based on age detection using novel logistic regression over support vector machine
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.
C.B.M Karthi, A. Kalaivani. Heart Disease Prediction Based on Age Detection using Novel Logistic Regression over Support Vector Machine. Cardiometry; Issue 25; December 2022; p.1711-1717; DOI: 10.18137/cardiometry.2022.25.17111717; Available from: https://www.cardiometry.net/issues/no25-december-2022/heart-disease-prediction-based