Machine Learning-Based Magnetocardiography Model Aids in Diagnosing Non-ST-Segment Elevation Acute Coronary Syndrome...

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.

To develop and validate a machine learning (ML)-based diagnostic model for NSTE-ACS using MCG-derived features.

Retrospective cohort study.

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.

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 LASSO-selected features, demonstrated the best performance, achieving an AUC of 0.91 ± 0.01 in nested CV. In the independent test set, the model achieved an AUC of 0.89 (95% confidence interval: 0.81-0.95), with an accuracy of 0.84, sensitivity of 0.89, and specificity of 0.77. Exploratory subgroup analyses showed consistent performance across age, sex, body mass index, and comorbidity groups. SHAP analysis identified the minimum magnetic field strength at the T-peak time (T_min_mag) as the most influential predictor.

The SVM-based MCG model showed strong potential as an auxiliary tool for identifying NSTE-ACS. Its application may improve chest pain management and reduce misdiagnoses.