Prediction of Heart Disease using Forest Algorithm over Linear Regression Algorithm using Machine Learning With Improved Accuracy

Abstract

Aim: To perform Predicting heart disease using the Forest algorithm and comparing its feature extraction precision with the Linear Regression Algorithm for improving the accuracy of the prediction. Methods and Materials: In the proposed work, Predicting heart disease was carried out using machine learning algorithms such as Linear Regression (n=10)and Forest algorithm(n=10). Here the pretest power analysis was carried out with 80% and the sample size for the two groups are 20. Results: From The implemented experiment, the Forest algorithm accuracy is 90.32% and the Linear Regression Algorithm 77.21%. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.001 (p<0.05) Conclusion: This study concludes that the Forest algorithm on patients healthcare analysis is significantly better than the Linear Regression Algorithm.