Supervised study of novel random forest algorithm for prediction of heart disease in comparison with the decision tree algorithm

Abstract

Aim: The aim of this work is to evaluate the accuracy and precision in predicting heart disease using Decision Tree (DT) and Novel Random forest (RF) Classification algorithms. Materials and Methods: Novel Random forest is appealed on a heart dataset which consists of 150 records. A framework for predicting heart disease in the medical field comparing the proposed and developed RF and DT classifiers. Sample Size Calculated as 55 in every group by using 80% G power. Sample Size Calculated using clinical analysis, with Alpha and Beta values ​​of 0.05 and 0.5, the confidence level. confidence is 95%, nicest strength is 80% and registration rate is 1. Results: The Decision Tree classifier produces 96.42% accuracy in predicting the heart disease on the data set, whereas the Random forest classifier predicts the same at the rate of 78.45% of the time with a statistically significant difference between the two groups (p=0.004;p<0.05)with confidence interval 95%. Hence Novel Random forest is better than the Decision Tree. Conclusion: The results show that the performance of Random forest is better compared with Decision Tree in terms of both precision and accuracy.

Imprint

P. Prasanna Sai Teja, Veeramani T. Supervised study of Novel Random Forest Algorithm for prediction of heart disease in Comparison With The Decision Tree Algorithm. Cardiometry; Issue 25; December 2022; p.1483-1490; DOI: 10.18137/cardiometry.2022.25.14831490; Available from: https://www.cardiometry.net/issues/no25-december-2022/supervised-study-novel-random-forest