Intelligent systems on the cloud for the early detection of chronic kidney disease

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.