Improving the Accuracy for Prediction of Heart Disease by Novel Feature Selection Scheme using Decision tree comparing with Naive-Bayes Classifier Algorithms

Aim: Two machine learning methods are employed in this study: DT and Naive Bayes. Heart disease detection and prediction can be improved by combining these two methods. Here are the components and steps: Heart disease can be predicted using the Decision Tree algorithm and the Naive Bayes approach. Both the Decision Tree and the Naive Bayes algorithms employ machine learning to make predictions about heart disease. I repeated this process 20 times to get the best results from heart disease images with a G power of 80 percent and a 0.05 percent threshold, the mean and standard deviation of which were in the 95 percent confidence interval (CI) 95 percent. This was necessary to get the best results. I have come to this conclusion after a lot of thought. It appears that when the Decision tree algorithm is compared to the Naive Bayes classifier algorithm, the Decision tree method outperforms the Naive Bayes classifier algorithm by a factor of 90.16 percent, according to the testing data. The Decision Tree classification algorithm outperforms the other classification algorithms, according on the data collected. the Naive Bayes classifier method in predicting heart disease.