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- W4367331201 abstract "Aim: To predict the heart disease using Forest Algorithm and comparing it with Decision Tree algorithm for improving the accuracy in predicting heart disease. Methods and Materials: Anticipating coronary illness expectation was completed utilising machine learning calculations, for example, Forest Algorithm and Decision tree. Here the pretest power analysis was carried out with 80% and the sample size for the two groups are 20. Results: Forest Algorithm accuracy is 90.00% while the Decision Tree algorithm has shown an accuracy of 85.00%. 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 accuracy is more significant and more accurate than the Decision Tree for predicting heart disease." @default.
- W4367331201 created "2023-04-30" @default.
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- W4367331201 date "2023-02-14" @default.
- W4367331201 modified "2023-10-01" @default.
- W4367331201 title "Prediction of Heart Disease using Forest Algorithm over Decision Tree using Machine Learning with Improved Accuracy" @default.
- W4367331201 doi "https://doi.org/10.18137/cardiometry.2022.25.15201525" @default.
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