Self-organization and associative memory: 3rd edition
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Ten lectures on wavelets
Data mining: practical machine learning tools and techniques with Java implementations
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Artificial Neural Networks: Theory and Applications
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Neural Networks in Chemistry and Drug Design
Neural Networks in Chemistry and Drug Design
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Clinical validation of machine learning for automatic analysis of multichannel magnetocardiography
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
An interactive framework for an analysis of ECG signals
Artificial Intelligence in Medicine
Data strip mining for the virtual design of pharmaceuticals with neural networks
IEEE Transactions on Neural Networks
Effects of discretization on determination of coronary artery disease using support vector machine
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Expert Systems with Applications: An International Journal
Using decision tree for diagnosing heart disease patients
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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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.