Heart Disease Prediction Based On Age Detection Using Novel Logistic Regression Over K-Nearest Neighbor

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.