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Original research

Analysis and comparison of prediction of heart disease using Novel K nearest neighbor and decision tree algorithm

Abstract

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.

Imprint

G. Pavithraa, Sivaprasad. Analysis and Comparison of Prediction of Heart Disease Using Novel K Nearest Neighbor and Decision Tree Algorithm. Cardiometry; Issue 25; December 2022; p.773-777; DOI: 10.18137/cardiometry.2022.25.773777; Available from: https://www.cardiometry.net/issues/no25-december-2022/analysis-comparison-prediction

Keywords

Novel k nearest neighbor,  Decision Tree,  Machine Learning,  Heart Disease,   Coronary Illness,  Samples,  Accuracy
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