Advancing Heart Disease Prediction through Machine Learning: Techniques and Insights for Improved Cardiovascular Health

Abstract

Heart disease persists as one of the leading causes of death in the USA and worldwide, accounting for a substantial proportion of global mortality. The significance of early detection of heart disease lies in its capability to counter catastrophic events such as strokes and heart attacks, which are often irreversible and fatal. Machine learning algorithms are gradually revolutionizing heart disease prediction since they can handle complex, multi-dimensional data sets. This research project used the Cleveland dataset from the UCI Machine Learning Repository, containing 70,000 records of patients with 12 unique features. Three machining learning algorithms were trained: Logistic Regression, Random Forest, and Support Vector Machines. Each algorithm was evaluated for precision, accuracy, recall, F1-score, and ROC-AUC. Based on the proof of the evaluation metrics for Logistic Regression, Random Forest, and SVM. In that respect, Logistic Regression was the best overall model since it yielded the highest ROC-AUC score, balancing true positives and false positives better than the rest of the models. The Support Vector Machine had the best accuracy, although it performed similarly to Logistic Regression but slightly lower. In retrospect, the implications for heart disease prediction are evident with simple algorithms such as Logistic Regression affirmatively performing better in specific early heart detection tasks, especially when balancing precision and recall. Indisputably, Machine learning models will have a high clinical impact on heart disease prediction since they enable early detection of heart diseases, leading to timely interventions and better patient prognoses.