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Saudi Journal of Kidney Diseases and Transplantation
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ORIGINAL ARTICLE Table of Contents   
Year : 2010  |  Volume : 21  |  Issue : 6  |  Page : 1073-1080
A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy


1 Department of Urology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
2 Department of Earth Sciences, Indian Institute of Technology, Mumbai, India

Correspondence Address:
Pratap B Singh
Department of Urology, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221 005
India
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PMID: 21060176

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To compare the accuracy of artificial neural network (ANN) analysis and multi­variate regression analysis (MVRA) for renal stone fragmentation by extracorporeal shock wave lithotripsy (ESWL). A total of 276 patients with renal calculus were treated by ESWL during December 2001 to December 2006. Of them, the data of 196 patients were used for training the ANN. The predictability of trained ANN was tested on 80 subsequent patients. The input data include age of patient, stone size, stone burden, number of sittings and urinary pH. The output values (predicted values) were number of shocks and shock power. Of these 80 patients, the input was analyzed and output was also calculated by MVRA. The output values (predicted values) from both the methods were compared and the results were drawn. The predicted and observed values of shock power and number of shocks were compared using 1:1 slope line. The results were calculated as coefficient of correlation (COC) (r2 ). For prediction of power, the MVRA COC was 0.0195 and ANN COC was 0.8343. For prediction of number of shocks, the MVRA COC was 0.5726 and ANN COC was 0.9329. In conclusion, ANN gives better COC than MVRA, hence could be a better tool to analyze the optimum renal stone fragmentation by ESWL.


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