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


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)


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:


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