Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department

OBJECTIVE: The study aimed to use machine learning algorithms to predict the need for revascularization in patients presenting with chest pain in the emergency department. METHODS: We obtained data from 581 patients with chest pain, 264 who underwent revascularization, and the other 317 were treated with medication alone for 3 months. Using standard algorithms, linear discriminant analysis, and standard algorithms, we analyzed 41 features relevant to coronary artery disease (CAD). RESULTS: We identified seven robust predictive features. The combination of these predictors gave an area under the curve (AUC) of 0.830 to predict the need for revascularization. By contrast, the GRACE score gave an AUC of 0.68. CONCLUSIONS: This machine learning-based approach predicts the need for revascularization in patients with chest pain.