Key result
Artificial neural network models, including back-propagation, generalized feedforward, and modular neural networks, were developed and compared to enable early detection of chronic kidney disease.
Why the study?
Can artificial neural network models accurately detect chronic kidney disease in its early stages?
Can artificial neural network models accurately detect chronic kidney disease in its early stages?
Artificial neural networks offer a potential tool for the early detection of chronic kidney disease by physicians and the public.
Supports ANN exploration for early CKD detection; leaves open need for prospective clinical validation.
This paper aims to construct intelligence models by applying the technologies of artificial neural networks including back-propagation network (BPN), generalized feedforward neural networks (GRNN), and modular neural network (MNN) that are developed, respectively, for the early detection of chronic kidney disease (CKD). The comparison of accuracy, sensitivity, and specificity among three models is subsequently performed. The model of best performance is chosen. By leveraging the aid of this system, CKD physicians can have an alternative way to detect chronic kidney diseases in early stage of a patient. Meanwhile, it may also be used by the public for self-detecting the risk of contracting CKD.
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Chiu et al. (2013) studied Chronic kidney disease (CKD). Artificial neural networks (BPN, GRNN, MNN) vs. Comparison among models was evaluated on Accuracy, sensitivity, and specificity. Artificial neural network models, including back-propagation, generalized feedforward, and modular neural networks, were developed and compared to enable early detection of chronic kidney disease.
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