Kidney Disease Predictor Based on Medical Decision Support System

Renal failure will increase mortality if untreated. When kidneys fail, the buildup of toxins occurs. Which affects the whole body and cause complications. There are numerous causes for renal failure, but we evaluate its main causes, which are Hypertension, Diabetes, Glomerulo Nephritis, Vesicoureteral Reflux and Polycystic Kidney Disease. Kidneys, in general, are very complicated organs. Nevertheless, most kidney diseases share a lot of presenting symptoms, which may lead to some delay in medical diagnosis. This study aims to develop a decision support system to predict the main cause of renal failure in patients using their memory, making quick predictions that may aid in the final diagnosis. A multilayer perceptron (MLP) feed-forward neural network was proposed in this research. The input layer of the proposed system included 32 input variables. An iterative process was used to determine the number of neurons and hidden layers. Furthermore, a resilient backpropagation algorithm (Rprop) was used to train the system. In order to access the generalization of the proposed system, a 10-fold cross-validation scheme was used. We obtained an encouraging result for prediction patient from the experiments made on the data that were taken from 180 patients' medical records at seven hospitals in Jordan.