Machine learning in diagnosing coronary artery disease via optical pumped magnetometer magnetocardiography: a prospective cohort study

Objective. The potential of optical pumped magnetometer magnetocardiography (OPM-MCG) for diagnosing coronary artery disease (CAD) has been initially shown, yet lacks large-scale prospective research. Approach. Using invasive coronary angiography (ICA) as a reference, we constructed three feature sets for the development of machine learning (ML) models: a ‘Heart’ feature set consisting only of OPM-MCG features, a ‘Clinical’ feature set, and a ‘Heart + Clinical’ combined feature set. We assessed the performance of 11 ML models with 10-fold cross-validation and conducted a feature importance analysis. Main results and Significance. Among 1513 participants (mean age 58.2 ± 12.0 years, 75.5% male), 1194 (78.92%) tested positive for ICA. Significant differences were observed in ‘Heart’ and ‘Clinical’ features between ICA-positive and negative groups. ML models using only ‘Heart’ features (AUC 0.84–0.88) outperformed those using only ‘Clinical’ features (AUC 0.62–0.75). Combining both feature types improved diagnostic accuracy (AUC 0.75–0.90). Feature importance analysis highlighted that ‘Significant change of Ar-PN’ in OPM-MCG was key for ICA diagnosis (47.8%), along with ‘Abnormal Sp-TT’, ‘Significant change of Ps-PN’, and ‘Abnormal Mg-TT’. OPM-MCG has high performance in diagnosing CAD, and the significant change of Ar-PN is the most important feature. Cat Boost and random forest are more suitable for OPM-MCG to build ML diagnostic models for CAD.