Machine Learning-Based Magnetocardiography Model Aids in Diagnosing Non-ST-Segment Elevation Acute Coronary Syndrome in Acute Chest Pain
Abstract
Background: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a leading cause of acute chest pain in clinical practice. Magnetocardiography (MCG) is a non-invasive and rapid functional imaging technique with high sensitivity to early, subtle electrophysiological changes associated with myocardial ischemia.
Aims: To develop and validate a machine learning (ML)-based diagnostic model for NSTE-ACS using MCG-derived features.
Study design: Retrospective cohort study.
Methods: Patients presenting with acute chest pain and admitted between September 2023 and May 2024 were consecutively enrolled. Pretreatment cardiac magnetic signals were collected using a 36-channel optically pumped magnetometer-based MCG system. A total of 13 feature categories (188 parameters) were extracted from the ST segment and T wave. Three feature selection methods [Boruta, least absolute shrinkage and selection operator (LASSO), and maximum relevance minimum redundancy], along with hyperparameter tuning and unbiased performance estimation for five ML algorithms, were implemented within a nested cross-validation (CV) framework. Model performance was assessed using the area under the curve (AUC). The optimal model was further validated in an independent test set. SHapley Additive exPlanations (SHAP) were used to interpret the final model.
Results: A total of 578 patients were included (366 with NSTE-ACS and 212 without NSTE-ACS). The support vector machine (SVM) model, based on nine…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 578 participants or records.
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