Abstract: Several urologic studies showed that it was important to determine the lithiasis types, in order to limit the recurrence residive risk and the renal function deterioration. The difficult problem posed by urologists for classifying urolithiasis is due to the large number of parameters (components, age, gender, background ...) taking part in the classification, and hence the probable etiology determination. There exist 6 types of urinary lithiasis which are distinguished according to their compositions (chemical components with given proportions), their etiologies and patient profile. This work presents models based on Boosted decision trees results, and which were compared according to their error rates and the runtime. The principal objectives of this work are intended to facilitate the urinary lithiasis classification, to reduce the classification runtime and an epidemiologic interest. The experimental results showed that the method is effective and encouraging for the lithiasis type identification.

Boutalbi Rafika, Farah Nadir, Chitibi Kheir Eddine, Boutefnouchet and Tanougast Camel, “Boosted Decision Trees for Lithiasis Type Identification” International Journal of Advanced Computer Science and Applications(IJACSA), 6(6), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060628

@article{Rafika2015,
title = {Boosted Decision Trees for Lithiasis Type Identification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060628},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060628},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {6},
author = {Boutalbi Rafika and Farah Nadir and Chitibi Kheir Eddine and Boutefnouchet and Tanougast Camel}
}