Self-Organization Maps for Prediction of Kidney Dysfunction
Ali Hussian Ali · 2008
� Abstract — This paper presents the prediction of Kidney dysfunction using Self Organization Maps (SOM). Six hundred and sixty three (663) 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 SOM model in which the training process is done by SOM. SOM which is a class of unsupervised 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 98%, 98% and 97% respectively for this proposed network .We conclude that 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.