Animesh Kumar Dubey*, Kavita Choudhary, Richa Sharma
Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 929-943, 2021, DOI:10.32604/iasc.2021.018382
Abstract Heart disease is a major health concern worldwide. The chances of recovery are bright if it is detected at an early stage. The present report discusses a comparative approach to the classification of heart disease data using machine learning (ML) algorithms and linear regression and classification methods, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), SVM with grid search (SVMG), k-nearest neighbor (KNN), and naive Bayes (NB). The ANOVA F-test feature selection (AFS) method was used to select influential features. For experimentation, two standard benchmark datasets of heart diseases, More >