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- W4367337204 abstract "Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and K-NN Algorithm. Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and. Each group consists of a sample size of 10 and the study parameters include alpha value 0.01, beta value 0.2, and the Gpower value of 0.8. Results: The Novel Logistic Regression (98.45) achieved improved accuracy than the K-NN Algorithm (79.82) in Heart Disease Prediction. The statistical significance difference is 0.01 (p<0.05). Conclusion: The Novel Logistic Regression model is significantly better than the K-NN Algorithm in Heart Disease Prediction. It can be also considered a better option for Heart Disease Prediction. Keywords Logistic Regression, Novel K-Nearest Neighbor, Heart disease, Artificial Intelligence, Computer Vision." @default.
- W4367337204 created "2023-04-30" @default.
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- W4367337204 date "2023-02-14" @default.
- W4367337204 modified "2023-09-30" @default.
- W4367337204 title "Heart Disease Prediction Based On Age Detection Using Novel Logistic Regression Over K-Nearest Neighbor" @default.
- W4367337204 doi "https://doi.org/10.18137/cardiometry.2022.25.17251730" @default.
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