Prediction Accuracy Improvement for Cardiovascular Diseases Using Machine Learning Algorithm

Worldwide Cardiovascular diseases (CVDs) are one of the most common causes of death. Multiple ML algorithms are employed in this work to address the CVD problem. The technique used in this study employs several machine-learning algorithms to predict CVD. To predict cardiac sickness, data mining approaches such as Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Ada Boosting, Gradient Boosting, and Bagging algorithms have been utilized. Using Base learners and Ensemble classifiers, this study tried to predict the presence of heart disease. Bagging and Gradient Boosting Classifiers had the highest accuracy, with 81.3% and 81.3%, respectively.