Analysis and comparison of prediction of heart disease using novel random forest and Naive Bayes algorithm

Cardiometry

article

G. Pavithraa¹  Sivaprasad¹ 

[1] Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University

关键词: Heart disease prediction;    Novel Random Forest;    Naive Bayes Algorithm;    Accuracy;    Enormous Data;    Statistical significance;    Samples;   

DOI  :  10.18137/cardiometry.2022.25.788793

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

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.

【 授权许可】

CC BY