Analysis of Accuracy in Heart Disease Diagnosis System Using Decision Tree Classifier Over Logistic Regression Based on Recursive Feature Selection

The aim of the research paper is to find the accuracy in a better way using Decision Tree compared with Logistic Regression by using Recursive Feature Elimination Technique. Materials and Methods: In this study, there are two groups, namely Decision Tree and Logistic Regression. Accuracy was computed for the dataset with sample size of 40. The innovative method used is Recursive Feature Elimination. It is used to find the subset of features which gives more accuracy. Result: It was observed that the Decision Tree algorithm obtains accuracy of 85.5% and Logistic Regression with 83.4% of accuracy. Decision Tree appears to have better significance than Logistic Regression technique with the value of p<0.05. The significant value obtained from statistical analysis is 0.01. Conclusion: The result proves that Decision Tree classifier shows better accuracy than Logistic Regression classifier based on Recursive Feature Selection.