Predicting Heart Disease with Random Forest: An In-Depth Analysis of Machine Learning Techniques for Early Detection and Risk Assessment

Heart disease is a major health concern, and this study investigates how machine learning, particularly Random Forest (RF), can be used to identify it early and assess risk. We tested various algorithms using a large dataset (BRFSS 2022) and found that RF performed best, achieving an accuracy of 93% and an AUC of 98%. To make the model run faster, we identified the four most important features and focused on those. This reduced accuracy slightly (86% for RF and Extra Trees) but with AUCs of 0.92 and 0.91 respectively. Importantly, training and testing times improved significantly. This trade-off between accuracy and speed makes these models more suitable for real-world use, potentially aiding healthcare professionals in the early detection and prevention of heart disease.