Key result
Artificial neural network models were developed for the early detection of chronic kidney disease, with the best-performing model deployed to the Google cloud platform for clinical and public use.
Why the study?
Can artificial neural network models deployed on a cloud platform accurately detect chronic kidney disease in its early stages?
Can artificial neural network models deployed on a cloud platform accurately detect chronic kidney disease in its early stages?
Artificial neural network models deployed on a cloud platform may provide an alternative and efficient method for the early detection of chronic kidney disease.
May facilitate accessible early CKD detection via cloud AI; leaves open need for prospective validation before clinical adoption.
This paper aims to construct intelligence models by applying the technologies of artificial neural networks including back-propagation network (BPN), generalized feed forward neural networks (GRNN), and modular neural network (MNN) 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 for system development. The system developed aligned with the best model is deployed to the Google cloud platform by leveraging Google Application Engine. By doing so, the result can more efficiently provide CKD physicians an alter native way to detect chronic kidney diseases in early stage of a patient. Meanwhile, it may also be used by publics for self-detecting the risk of contracting CKD.
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Chiu et al. (2012) studied Chronic kidney disease. Artificial neural network models (BPN, GRNN, MNN) vs. Comparison among models was evaluated on Accuracy, sensitivity, and specificity. Artificial neural network models were developed for the early detection of chronic kidney disease, with the best-performing model deployed to the Google cloud platform for clinical and public use.
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