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- W4367331488 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." @default.
- W4367331488 created "2023-04-30" @default.
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- W4367331488 date "2023-02-14" @default.
- W4367331488 modified "2023-09-30" @default.
- W4367331488 title "Analysis and Comparison of Prediction of Heart Disease Using Novel K Nearest Neighbor and Decision Tree Algorithm" @default.
- W4367331488 doi "https://doi.org/10.18137/cardiometry.2022.25.773777" @default.
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