Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and Decision tree Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and Decision tree 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 0.8. Results: The Novel Logistic Regression (91.60) achieved improved accuracy than the Decision Tree (89.42) 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 Decision Tree in Heart Disease Prediction. It can be also considered as a better option for Heart Disease Prediction.
Aim: To improve the accuracy in Heart Disease Prediction using Logistic Regression and Random Forest. Materials and Methods: This study contains 2 groups i.e Logistic Regression and Random Forest. 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 Logistic Regression achieved improved accuracy of 91.60 then the Random Forest in Heart Disease Prediction. The statistical significance difference is 0.01 (p<0.05). Conclusion: The Logistic Regression model is significantly better than the Random Forest in Heart Disease Prediction. It can be also considered a better option for Heart Disease Prediction. deviation (0.08600,0.09333)
Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and Support Vector Machine. Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and Support Vector Machine. 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 (91.60) achieved improved accuracy than the Support Vector Machine (91.83) in Heart Disease Prediction. The statistical significance difference (two-tailed) is 0.01 (p<0.05). Conclusion: The Novel Logistic Regression model is significantly better than the Support Vector Machine in Heart Disease Prediction. It can be also considered a better option for Heart Disease Prediction.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.