An Efficient Ensemble Machine Learning Model for Cardiovascular Disease Prediction Using Digital Health Records

Abstract

Abstract: Detecting cardiovascular problems early is crucial for timely treatment. In our study, we employed machine learning to analyze a diverse set of information about individuals' lives and health, to predict cardiovascular disease. Ensuring data accuracy and addressing missing information were prioritized in our approach. Experimenting with different solo ML & ensemble ML methods, comprised of Random Forest and XGBoost with tuning, we achieved a notable 92% accuracy in identifying potential heart issues. Remarkably, combining multiple machine learning methods through ensemble learning proved even more effective than individual methods. Expanding our methodology to include Light GBM, Extra Tree, Decision Tree, SVM, Naive Bayes, QDA, & Adaboost enhanced the comprehensiveness of our analysis. Additionally, delving into ensemble learning methods such as bagging, boosting, tuning, & stacking further pushed the boundaries of predictive accuracy. In essence, our research outstands the potency of diverse ensemble machine-learning techniques and algorithms in early cardiovascular prediction. Ensemble methods, which combine different algorithms, emerged as powerful tools without relying on complex terminology.