Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multilayered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Further, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.
 Walter A. Hunt, Kidney Diseases, A Guide for Living, Forwarded by Ronald D. Perrone, M.D. Tufts Medical Center.
 Comprehensive Clinical Nephrology by Jurgen Floege, Richard J. Johnson, MD and John Feehally.
 Artificial Neural Networks - Methodological Advances and Biomedical Applications, Edited by Kenji Suzuki, ISBN 978-953-307-243-2, hard cover, 362 pages, Publisher: InTech, Published: April 11.
 Clinical Applications of Artificial Neural Networks. Edited by Richard Dybowski King's College London Edited by Vanya Gant University College London Hospitals NHS Trust, London.
 B. Yegnanarayana, Artificial Neural Networks by Prentice Hall.
 T. Kohonen, Self-organisation and Associative Memory, Springer Verlag, Berlin, 1988.
 Introduction to multi-layer feed-forward neural networks by Daniel Svozil a, Vladimir KvasniEka b, JiE Pospichal b Received 15 October 1996; revised 25 February 1997; accepted 6 June 1997
 Laurene Fausett, Florida Institute of Technology, Fundamentals of Neural Networks Architectures, Algorithm, and Applications. Pearson Publications.
 Oxford, UK. Box, G.E.P. and Jenkins, G.M. (1970) Time Series Analysis, Forecasting and Control, Holden Day, San Francisco, CA.
 S. N. Sivanandam, S. Sumathi, S. N. Deepa, Introduction to Neural Networks using MATLAB6.0, TATA McGRAW HILL.