Comparative Evaluation of Machine Learning Models for Short- and Long-Term Prediction of Major Adverse Cardiovascular Events

Abstract

Acute coronary syndromes (ACS) are a leading cause of morbidity and mortality worldwide, emphasizing the urgent need for reliable prediction of major adverse cardiovascular events (MACE) to support timely clinical decision-making. This study develops and validates machine learning (ML) models for MACE prediction using retrospective data from 2,721 ACS patients. A total of 109 predictor variables were extracted, encompassing demographics, comorbidities, and clinical parameters. A natural language processing (NLP) scheme was developed to transform the unstructured clinical text into quantitative scores. Five MLs, including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Neural Network (NN), and Logistic Regression (LR), were developed to predict MACE across 30-day, 1-year, and 3-year horizons. RF consistently outperformed other models with Area Under the Curves (AUCs) ranging from 0.82 to 0.87, and achieved the highest aggregated performance rank across six evaluation metrics. This performance surpasses that of prior studies, which reported AUCs between 0.71 and 0.81. Cohort analysis revealed a progressive increase in MACE prevalence and consistent clinical differences between MACE and non-MACE patients, including older age, comorbidities, and abnormal vital and laboratory values. The RF’s strong performance supports its clinical utility in both acute triage and long-term cardiovascular risk monitoring. The proposed framework holds promise for enhancing patient outcomes, optimizing healthcare resource allocation, and reducing costs through informed and timely interventions.

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