Precision Medicine in Cardiology: ML-based Heart Disease Prediction

Heart disease remains among the leading causes of mortality all over the world, and early detection is required to improve the prognosis of patients. Traditional methods of detection are not necessarily accurate or effective, thus the demand for advanced predictive models. This research addresses the challenge of advancing heart disease predictive precision using machine learning algorithms, i.e., Random Forest and Decision Trees. A cardiovascular dataset was processed in advance to deal with missing values, detection of outliers, and normalization in order to have data quality ensured. Models were compared against accuracy, precision, recall, F1-score, and AUC, and Random Forest proved 85% accuracy, more reliable than Decision Trees as it was not overfitted. The work illustrates predictability vs. interpretability and indicates feature importance analysis to favour clinician confidence. The outcomes are decisive that machine learning has the potential to augment early intervention and diagnosis planning in cardiology and deliver data-based clinical decision-making and risk assessment.