Analysis and comparison of prediction of heart disease using Novel K nearest neighbor and decision tree 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: Novel k nearest neighbor;    Decision Tree;    Machine Learning;    Heart Disease;    Coronary Illness;    Samples;    Accuracy;   

DOI  :  10.18137/cardiometry.2022.25.773777

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

Aim: Prediction of coronary illness utilizing novel Novel k nearest neighbor (KNN) and contrasting its accuracy with decision tree algorithm. Materials and Methods : Two gatherings are proposed for foreseeing the accuracy (%) of coronary illness. To be specific, novel Novel k nearest neighbor and decision tree algorithm. Here we take 20 examples each for assessment and look at. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The decision tree gives better accuracy (84.95%) contrasted with the Novel k nearest neighbor accuracy (76.29 %). Along these lines the factual meaning of the decision tree is superior to the novel k nearest neighbor algorithm with significance value of 0.115. Conclusion : From the outcome, it may very well be inferred that the decision tree helps in anticipating the coronary illness with more precision contrasted with the novel k nearest neighbor algorithm.

【 授权许可】

CC BY