Harnessing Ensemble in Machine Learning for Accurate Early Prediction and Prevention of Heart Disease

Cardiovascular diseases (CVDs) remain a significant global health concern, demanding precise and early prediction methods for effective intervention. In this comprehensive study, various machine learning algorithms were rigorously evaluated to identify the most accurate approach for forecasting heart disease. Through meticulous analysis, it was established that precision, recall, and the F1-score are critical metrics, overshadowing the mere accuracy of predictions. Among the classifiers explored, the Decision Tree (DT) and Random Forest (RF) algorithms emerged as the most proficient, boasting remarkable accuracy rates of 96.75%. The DT Classifier exhibited a precision rate of 97.81% and a recall rate of 95.73%, resulting in an exceptional F1-score of 96.76%. Similarly, the RF Classifier achieved an outstanding precision rate of 95.85% and a recall rate of 97.88%, yielding an exemplary F1-score of 96.85%. In stark contrast, other methods, including Logistic Regression, Support Vector Machine, and K-Nearest Neighbor, demonstrated inferior predictive capabilities. This study conclusively establishes the combination of Decision Tree and Random Forest algorithms as the most potent and dependable approach for predicting cardiac illnesses, providing a groundbreaking avenue for early intervention and personalized patient care. These findings signify a significant advancement in the field of predictive healthcare analytics, offering a robust framework for enhancing healthcare strategies related to cardiovascular diseases.