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Original research

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.

Imprint

G. Pavithraa, Sivaprasad. Analysis And Comparison Of Prediction Of Heart Disease Using Novel Random Forest And Naive Bayes Algorithm. Cardiometry; Issue 25; December 2022; p.788-793; DOI: 10.18137/cardiometry.2022.25.788793; Available from: https://www.cardiometry.net/issues/no25-december-2022/naive-bayes-algorithm

Keywords

Heart disease prediction,  Novel Random Forest ,  Naive Bayes Algorithm,  Accuracy,  Enormous Data,  Statistical significance,  Samples
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