Publication: Data mining of magnetocardiograms for prediction of ischemic heart disease
Issued Date
2010-12-01
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16112156
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2-s2.0-80455137093
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Mahidol University
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SCOPUS
Bibliographic Citation
EXCLI Journal. Vol.9, (2010), 82-95
Suggested Citation
Yosawin Kangwanariyakul, Chanin Nantasenamat, Tanawut Tantimongcolwat, Thanakorn Naenna Data mining of magnetocardiograms for prediction of ischemic heart disease. EXCLI Journal. Vol.9, (2010), 82-95. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/28421
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Title
Data mining of magnetocardiograms for prediction of ischemic heart disease
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Abstract
Ischemic Heart Disease (IHD) is a major cause of death. Early and accurate detection of IHD along with rapid diagnosis are important for reducing the mortality rate. Magnetocardiogram (MCG) is a tool for detecting electro-physiological activity of the myocardium. MCG is a fully non-contact method, which avoids the problems of skin-electrode contact in the Electrocardiogram (ECG) method. However, the interpretation of MCG recordings is time-consuming and requires analysis by an expert. Therefore, we propose the use of machine learning for identification of IHD patients. Back-propagation neural network (BPNN), the Bayesian neural network (BNN), the probabilistic neural network (PNN) and the support vector machine (SVM) were applied to develop classification models for identifying IHD patients. MCG data was acquired by sequential measurement, above the torso, of the magnetic field emitted by the myocardium using a J-T interval of 125 cases. The training and validation data of 74 cases employed 10-fold cross-validation methods to optimize support vector machine and neural network parameters. The predictive performance was assessed on the testing data of 51 cases using the following metrics: accuracy, sensitivity, and specificity and area under the receiver operating characteristic (ROC) curve. The results demonstrated that both BPNN and BNN displayed the highest and the same level of accuracy at 78.43%. Furthermore, the decision threshold and the area under the ROC curve was -0.2774 and 0.9059, respectively, for BPNN and 0.0470 and 0.8495, respectively, for BNN. This indicated that BPNN was the best classification model, BNN was the best performing model with sensitivity of 96.65%, and SVM employing the radial basis function kernel displayed the highest specificity of 86.36%.