A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy
Joint Authors
Singh, T. N.
Singh, Pratap Bahadur
Goyal, Neeraj K.
Kumar, Abhay
Dwivedi, Udai Shankar
Trivedi, Samir
Source
Saudi Journal of Kidney Diseases and Transplantation
Issue
Vol. 21, Issue 6 (31 Dec. 2010), pp.1073-1080, 8 p.
Publisher
Saudi Center for Organ Transplantation
Publication Date
2010-12-31
Country of Publication
Saudi Arabia
No. of Pages
8
Main Subjects
Topics
Abstract EN
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.
American Psychological Association (APA)
Goyal, Neeraj K.& Kumar, Abhay& Trivedi, Samir& Dwivedi, Udai Shankar& Singh, T. N.& Singh, Pratap Bahadur. 2010. A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy. Saudi Journal of Kidney Diseases and Transplantation،Vol. 21, no. 6, pp.1073-1080.
https://search.emarefa.net/detail/BIM-223301
Modern Language Association (MLA)
Goyal, Neeraj K.…[et al.]. A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy. Saudi Journal of Kidney Diseases and Transplantation Vol. 21, no. 6 (Dec. 2010), pp.1073-1080.
https://search.emarefa.net/detail/BIM-223301
American Medical Association (AMA)
Goyal, Neeraj K.& Kumar, Abhay& Trivedi, Samir& Dwivedi, Udai Shankar& Singh, T. N.& Singh, Pratap Bahadur. A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy. Saudi Journal of Kidney Diseases and Transplantation. 2010. Vol. 21, no. 6, pp.1073-1080.
https://search.emarefa.net/detail/BIM-223301
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 1080
Record ID
BIM-223301