Prediction of Heart Disease using Forest Algorithm over Decision Tree using Machine Learning with Improved Accuracy

Abstract

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.