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Prediction of heart disease using forest algorithm over decision tree using machine learning with improved accuracy


Aim: To predict the heart disease using Forest Algorithm and comparing it with Decision Tree algorithm for improving the accuracy in predicting heart disease. Methods and Materials: Anticipating coronary illness expectation was completed utilising machine learning calculations, for example, Forest Algorithm and Decision tree. Here the pretest power analysis was carried out with 80% and the sample size for the two groups are 20. Results: Forest Algorithm accuracy is 90.00% while the Decision Tree algorithm has shown an accuracy of 85.00%. There is a measurable 2-tailed significant distinction in exactness for two calculations is 0.001 (p<0.05) by performing independent samples T-tests. Conclusion: The Forest Algorithm accuracy is more significant and more accurate than the Decision Tree for predicting heart disease.


K N S Shanmukha Raj, K Thinakaran. Prediction of Heart Disease using Forest Algorithm over Decision Tree using Machine Learning with Improved Accuracy. Cardiometry; Issue 25; December 2022; p.1520-1525; DOI: 10.18137/cardiometry.2022.25.15201525; Available from:


Machine Learning,   Forest Algorithm,   Prediction of Heart Disease,   Supervised Classification,   Novel Principal Component Analysis,   Decision Tree
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