Identification of ischemic heart disease via machine learning analysis on magnetocardiograms

• Published in: Computers in biology and medicine Vol. 38; no. 7; pp. 817 - 825
• Main Authors: Tantimongcolwat, Tanawut, Naenna, Thanakorn, Isarankura-Na-Ayudhya, Chartchalerm, Embrechts, Mark J, Prachayasittikul, Virapong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2008; Elsevier Limited
• Subjects: Back-propagation neural network; Humans; Internal Medicine; Ischemia; Learning; Machine learning; Magnetocardiography; Magnetocardiography - methods; Myocardial Ischemia - diagnosis; Other; Reproducibility of Results; Self-organizing map; Sensitivity and Specificity; Back-propagation neural network; Magnetocardiography; Ischemia; Self-organizing map; Machine learning
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2008.04.009

Abstract Ischemic heart disease (IHD) is predominantly the leading cause of death worldwide. Early detection of IHD may effectively prevent severity and reduce mortality rate. Recently, magnetocardiography (MCG) has been developed for the detection of heart malfunction. Although MCG is capable of monitoring the abnormal patterns of magnetic field as emitted by physiologically defective heart, data interpretation is time-consuming and requires highly trained professional. Hence, we propose an automatic method for the interpretation of IHD pattern of MCG recordings using machine learning approaches. Two types of machine learning techniques, namely back-propagation neural network (BNN) and direct kernel self-organizing map (DK-SOM), were applied to explore the IHD pattern recorded by MCG. Data sets were obtained by sequential measurement of magnetic field emitted by cardiac muscle of 125 individuals. Data were divided into training set and testing set of 74 cases and 51 cases, respectively. Predictive performance was obtained by both machine learning approaches. The BNN exhibited sensitivity of 89.7%, specificity of 54.5% and accuracy of 74.5%, while the DK-SOM provided relatively higher prediction performance with a sensitivity, specificity and accuracy of 86.2%, 72.7% and 80.4%, respectively. This finding suggests a high potential of applying machine learning approaches for high-throughput detection of IHD from MCG data.