Detection of coronary artery disease in patients with chest pain: A machine learning model based on magnetocardiography parameters

Abstract

BACKGROUD: Patients with chest pain and suspected of coronary artery disease(CAD) need further test to confirm the diagnosis. Magnetocardiography (MCG) is a non-invasive and emission-free technology which can detect and measure the weak magnetic fields created by the electrical activity of the heart. OBJECTIVE: This study aimed to investigate the usefulness of the 10 MCG parameters to detect CAD in patients with chest pain by means of a machine learning method of multilayer perceptron(MLP) neural network. METHODS: 209 patients who were suffering from chest pain and suspected of CAD were enrolled in this cross-sectional study. In all patients, 12-lead electrocardiography(ECG) and MCG test were performed before percutaneous coronary angiography(PCA). 10 MCG parameters were analyzed by MLP neural networks. RESULTS: 11 diagnostic models(M1 to M11) were established after MLP analysis. The accuracies ranged from 71.2% to 90.5%. Two models(M10 and M11) were further analyzed. The accuracy, sensitivity, specificity, PPV, NPV, PLR and NLR were 89.5%, 89.8%, 88.9%, 92.7%, 84.7%, 11.10 and 0.11, of M10, and were 90.0%, 91.4%, 87.7%, 92.1%, 86.6%, 7.43 and 0.10, of M11. CONCLUSIONS: By a method of MLP neural network, MCG is applicable in identifying CAD in patients with chest pain, which seems beneficial for detection of CAD.