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An innovative penalty based heart disease prediction system using novel random forest over logistic regression classifier algorithm


Aim:The main goal of the research is see how accurately predicting heart disease by Logistic Regression (LR) and Novel Random Forest(RF) Classifications. Materials and Methods: Novel Random forest appealed on a heart dataset which consists of 200 recordsA framework for predicting heart disease in the medical field has been proposed and developed to compare the RF with a LR classifier. The sample size was calculated to be 55 for each group with 80% G performance. The sample size was calculated using a Clincalc analysis with Alpha and Beta values ​​of 0.05 and 0.5, pretest performance of 80%, and enrollment rate of 1. The Accuracy of the classifier was Evaluated and Recorded. Results: The LR produces 89.0% in predicting the heart disease on the data set used whereas the Novel Random forest classifier predicts the same at the rate of 95.46% of the time with a statistically significant difference between the two groups (P=0.03; P<0.05) with confidence interval 95%. Conclusion: RF is better compared with LR in terms of both precision and accuracy.


P. Prasanna Sai Teja, Veeramani T. An Innovative Penalty based Heart Disease Prediction system using Novel Random Forest over Logistic Regression Classifier Algorithm. Cardiometry; Issue 25; December 2022; p.1477-1482; DOI: 10.18137/cardiometry.2022.25.14771482; Available from:


Novel Random Forest,   Logistic Regression,   Data Mining,   Blood pressure,   Pulse rate,   Heart Disease,   Classification
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