Prediction of Heart Disease using Decision Tree over Logistic Regression using Machine Learning with Improved Accuracy

Abstract

Aim: Predicting heart disease using the Decision Tree and comparing its feature extraction precision with the Logistic 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 Logistic Regression (n=10) and Decision tree (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 Decision Tree accuracy significantly better than the Logistic Regression 80.10%. There is a measurable 2-tailed huge distinction in accuracy for two algorithms is 0.001 (p<0.05) Conclusion: The Decision Tree algorithm got better accuracy than Logistic Regression for Predicting heart disease.