Heart disease prediction based on age detection using novel logistic regression over k-nearest neighbor
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.
C.B.M.Karthi, A. Kalaivani. Heart Disease Prediction Based On Age Detection Using Novel Logistic Regression Over K-Nearest Neighbor. Cardiometry; Issue 25; December 2022; p.1725-1730; DOI: 10.18137/cardiometry.2022.25.17251730; Available from: https://www.cardiometry.net/issues/no25-december-2022/regression-over-k-nearest-neighbor