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Expert Systems with Applications: An International Journal
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Genetic Programming and Evolvable Machines
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Artificial Intelligence in Medicine
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Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
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Information Sciences: an International Journal
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Expert Systems with Applications: An International Journal
Review: Application of artificial neural networks in the diagnosis of urological dysfunctions
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Genetic Programming and Evolvable Machines
Expert Systems with Applications: An International Journal
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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Expert Systems with Applications: An International Journal
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HIKM '12 Proceedings of the Fifth Australasian Workshop on Health Informatics and Knowledge Management - Volume 129
Hi-index | 12.05 |
Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients' physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients.