ANN-Based Prediction of Kidney Dysfunction Using Clinical Laboratory Data

Abstract

This paper presents the prediction of Kidney dysfunction using probabilistic neural network (PNN). Six hundred and sixty (660) sets of analytical laboratory test have been collected from one of the private Clinical laboratories in Baghdad. For each subject, Serum urea and Serum creatinin levels have been analyzed and

tested by using clinical laboratory measurements. The collected Urea and cretinine levels are then used as inputs to the Artificial Neural network model in which the training process is done by PNN which is a class of radial basis function (RBF) network is used as a classifier to predict whether Kidney is normal or it will have a dysfunction. The accuracy of Prediction, sensitivity and Specificity were found to be equal to 99%, 98% and 99% respectively for this proposed network

.We conclude that the proposed model gives faster and more accurate prediction of Kidney dysfunction and it works as promising tool for predicting of routine kidney dysfunction from the clinical laboratory data.