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- W4367331301 abstract "Aim: To perform Predicting heart disease using the Forest algorithm and comparing its feature extraction precision with the K-nearest neighbors algorithm for working on the precision of the forecast. Materials and Methods: In the proposed work, Predicting heart disease was carried out using machine learning algorithms such as K-nearest neighbors algorithm (n=10) and Forest Algorithm (n=10). Here the pretest power examination was done with gpower 80% and the sample size for the two gatherings was 20. Results: From The implemented experiment, the Forest algorithm accuracy is significantly better and it is 90.0% than the K-nearest neighbors algorithm 83.00%. There is a measurable 2-tailed significant distinction in exactness for two calculations is 0.001 (p<0.05) by performing Independent samples T-tests. Conclusion: The Forest algorithm got better Accuracy and classification of digits better than K-nearest neighbors algorithm for Predicting heart disease." @default.
- W4367331301 created "2023-04-30" @default.
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- W4367331301 date "2023-02-14" @default.
- W4367331301 modified "2023-10-16" @default.
- W4367331301 title "Prediction of Heart Disease using Forest Algorithm over K-nearest neighbors using Machine Learning with Improved Accuracy" @default.
- W4367331301 doi "https://doi.org/10.18137/cardiometry.2022.25.15001506" @default.
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