Abstract
Diabetes is a disease in which the blood glucose, or blood sugar levels in the body are too high. The damage caused by diabetes can be very severe and even more pronounced in pregnant women due to the tendency of transmitting the hereditary disease to the next generation. Expert systems are now used in medical diagnosis of diseases in patients so as to detect the ailment and help in providing a solution to it. This research developed and trained a neural network model for the diagnosis of diabetes mellitus in pregnant women. The model is a four-layer feed forward network, trained using back-propagation and Bayesian Regulation algorithm. The input layer has 8 neurons, two hidden layers have 10 neurons each, and the output layer has one neuron which is the diagnosis result. The developed model was also incorporated into a web-based application to facilitate its use. Validation by regression shows that the trained network is over 92% accurate.
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Acknowledgement
We acknowledge the support and sponsorship provided by Covenant University through the Centre for Research, Innovation and Discovery (CUCRID).
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Alade, O.M., Sowunmi, O.Y., Misra, S., Maskeliūnas, R., Damaševičius, R. (2018). A Neural Network Based Expert System for the Diagnosis of Diabetes Mellitus. In: Antipova, T., Rocha, Á. (eds) Information Technology Science. MOSITS 2017. Advances in Intelligent Systems and Computing, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-319-74980-8_2
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DOI: https://doi.org/10.1007/978-3-319-74980-8_2
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