Analysis And Comparison Of Prediction Of Heart Disease Using Novel Random Forest And Naive Bayes Algorithm

Abstract

Aim : Prediction of heart disease using Novel Random Forest and comparing its accuracy with Naive Bayes algorithm. Materials and methods: Two groups are proposed for predicting the accuracy (%) of heart disease. Namely, the Novel Random Forest and Naive Bayes algorithm. Here we take 20 samples each for evaluation and compared. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The Novel Random Forest gives better accuracy (86.40%) compared to the Naive Bayes accuracy (80.08%). Therefore the statistical significance of Novel Random Forest is better than Naive Bayes algorithm. Conclusion: From the result, it can be concluded that Novel Random Forest helps in predicting heart disease with more accuracy compared to Naive Bayes algorithm.