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- W4367335937 abstract "Aim: Comparison of accuracy rate in prediction of cardiovascular disease using Novel Random Forest with Logistic Regression. Materials and Methods: The Novel Random forest (N=20) and Novel Logistic Regression Algorithm (N=20) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work.The sample size is determined using the G power Calculator and it’s found to be 10. Results: The Random Forest exhibited 89.06% accuracy whilst a Logistic Regression has shown 92.18%. accuracy. Statistical significance difference between Random forest algorithm and Novel Logistic Regression Algorithm was found to be p=0.001 (2 tailed) (p<0.5). Conclusion: Prediction of cardiovascular disease using Logistic Regression is significantly better than the Random Forest." @default.
- W4367335937 created "2023-04-30" @default.
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- W4367335937 date "2023-02-14" @default.
- W4367335937 modified "2023-09-30" @default.
- W4367335937 title "Comparison of Accuracy Rate in Prediction of Cardiovascular Disease using Random Forest with Logistic Regression" @default.
- W4367335937 doi "https://doi.org/10.18137/cardiometry.2022.25.15261531" @default.
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