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

Junting Li^1,2,3, Yuheng Zhou^1,2,3, Ruizhe Wang^1,2,3, Jiaojiao Pang^3,4, Min Xiang^{2,3,5,6,7,8,9}, Xiaolin Ning¹

¹Beihang University School of Instrumentation and Optoelectronic Engineering, Beijing, China
²Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, Beihang University School of Instrumentation and Optoelectronic Engineering, Beijing, China
³National Innovation Platform for Industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Research Institute of Shandong University, Jinan, China
⁴Department of Emergency Medicine, Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China
⁵State Key Laboratory of Traditional Chinese Medicine Syndrome, National Institute of Extremely-weak Magnetic Field Infrastructure, Hangzhou, China
⁶Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou, China
⁷Zhejiang Key Laboratory of Zero Magnetic Medicine, Hangzhou, China
⁸Hefei National Laboratory, Hefei, China
⁹Hangzhou Lingci Medical Equipment Co. Ltd, Hangzhou, China

DOI : 10.4274/balkanmedj.galenos.2026.2026-1-319

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 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.

Conclusion: 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.

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