Predicate the Ability of Extracorporeal Shock Wave Lithotripsy (ESWL) to treat the Kidney Stones by used Combined Classifier

, Samera Shams Hussein; , Lubab Ahmed Tawfeeq; , Sukaina Sh Altyar

	Predicate the Ability of Extracorporeal Shock Wave Lithotripsy (ESWL) to treat the Kidney Stones by used Combined Classifier
Journal of Al-Qadisiyah for Computer Science and Mathematics
Article 1, Volume 11, Issue 1, 2019, Pages 41-52 PDF (0 K)
Authors
Samera Shams Hussein; Lubab Ahmed Tawfeeq; Sukaina Sh Altyar
Abstract
Extracorporeal Shock Wave Lithotripsy (ESWL) is the most commonplace remedy for kidney stone. Shock waves from outside the body frame are centered at a kidney stone inflicting the stone to fragment. The success of the (ESWL) treatment is based on some variables such as age, sex, stone quantity stone period and so on. Thus, the prediction the success of remedy by this method is so important for professionals to make a decision to continue using (ESWL) or to using another remedy technique. In this study, a prediction system for (ESWL) treatment by used three techniques of mixing classifiers, which is Product Rule (PR), Neural Network (NN) and the proposed classifier called Nested Combined Classifier (NCC). The samples had been taken from 2850 actual sufferers cases that had been treated at Urology and Nephrology center of Iraq. The results from three cases have been compared to actual treatment results of (ESWL) for trained and nontrained cases and compared the results of three models. The results show that (NCC) approach is the most accurate method in prediction the efficient of uses (ESWL) remedy in treatment the kidney stone.
Keywords
Extracorporeal Shock Wave Lithotripsy; Product Rule; neural network; ANN

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