Abstract:
This research intents to assess the application of artificial neural network
in predicting the kidney failure disease. Kidney failure disease is being
observed as a serious challenge to the field of medical with its impact on
a mass population of the world. This work explored and analyzed the data
generated from 60 kidney patients in many hospitals and hemodialysis
centers using data mining technique .
This is done by using Artificial Neural Network technique to select the
weight, and connectivity structure to determine system for input variables
learning. This work provides Physicians with an instrument assess the
dialysis service performance
The study for prediction of kidney failure has been carried out using Feed
forward back propagation and Cascade forward back propagation
algorithms.
The results of this study demonstrate that an ANN model with variables
consisting of (urea, creatinine, potassium, sodium, calcium, phosphorus
and uric acid) is classified a total of 60 patients correctly to normal and
abnormal.
The best network model produced prediction accuracy of 98.3 percent is
given by Feed Forward Back Propagation network (FFBP) .