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

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.

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.

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.

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.

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.