Heart disease prediction based on age detection using logistic regression over random forest

C.B.M Karthi¹, A. Kalaivani¹

¹ Department of Information Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu, India

Abstract

Aim: To improve the accuracy in Heart Disease Prediction using Logistic Regression and Random Forest. Materials and Methods: This study contains 2 groups i.e Logistic Regression and Random Forest. 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 Logistic Regression achieved improved accuracy of 91.60 then the Random Forest in Heart Disease Prediction. The statistical significance difference is 0.01 (p<0.05). Conclusion: The Logistic Regression model is significantly better than the Random Forest in Heart Disease Prediction. It can be also considered a better option for Heart Disease Prediction. deviation (0.08600,0.09333)

Imprint

C.B.M Karthi, A. Kalaivani. Heart Disease Prediction Based on Age Detection Using Logistic Regression over Random Forest. Cardiometry; Issue 25; December 2022; p.1731-1737; DOI: 10.18137/cardiometry.2022.25.17311737; Available from: https://www.cardiometry.net/issues/no25-december-2022/logistic-regression-over-random-forest

Keywords

Logistic Regression, Novel Random Forest, Heart disease prediction, Accuracy, Machine Learning

Download PDF

Current issue