@article{(Open Science Index):https://publications.waset.org/pdf/12166,
	  title     = {Comparison of Different Neural Network Approaches for the Prediction of Kidney Dysfunction},
	  author    = {Ali Hussian Ali AlTimemy and  Fawzi M. Al Naima},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests 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 three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are 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 for each type of the proposed networks
.We conclude that PNN 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.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {5},
	  number    = {4},
	  year      = {2011},
	  pages     = {147 - 153},
	  ee        = {https://publications.waset.org/pdf/12166},
	  url   	= {https://publications.waset.org/vol/52},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 52, 2011},
	}