Paper title:

Disordered Metabolic Evaluation in Renal Stone Recurrence: A Data Mining Approach

Published in: Issue 2, (Vol. 5) / 2011
Publishing date: 2011-10-28
Pages: 64-68
Author(s): TAGHI ADL Seyyed, GIVCHI Arash, SARAEE Mohamad, ESHRAGHI Amid
Abstract. Nephrolithiasis is a disease with a high and even rising incidence. It has a high morbidity, generates high costs and has a high recurrence rate. Metabolic evaluation in renal stone formers allows the identification and quantification of risk factors and establishment of individual risk profiles. Based on these individuals risk profiles, rational therapy for metaphylaxis of renal stones lowers stone recurrence rate significantly. The purpose of this article is metabolic investigation in patients with nephrolithiasis in Isfahan city- Iran. Different data mining algorithms such as Clustering and Classification were employed for extracting knowledge in the form of decision rules. These results evaluate the risk of morbidity and recurrence of the diseases. Some medical attributes gathered based on their medical importance. The data mining tasks applied in this research have been applied and tested over 406 observed samples collected at different clinics in the city of Isfahan.
Keywords: Renal Stone Recurrence, Association Rules, Clustering, Classification

1. Khabaza, T.; Shearer, C.; "Data mining with Clementine," Knowledge Discovery in Databases, IEE Colloquium on. , vol., no., pp.1/1-1/5, 2 Feb 1995

2. Agrawal R, “Mining association rules between sets of items in large databases”, Proceeding of the 1993 ACM SIGMOD Conference, Washington, pp. 207-216, November 1993.

3. Julio F. Navarro, Carlos S. Frenk and Simon D. M. White “A Universal Density Profile from Hierarchical Clustering”, THE ASTROPHYSICAL JOURNAL, 490:493E508, 1997 December 1

4. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, University of California Press, 1, 281- 297.

5. links/1304-data-mining-in-matlab 2010-08-23

6. K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B.Scholkopf, “An introduction to kernel-based learning algorithms,” IEEE Trans. Neural Networks, vol. 12, no. 2, pp.181-201, 2001. 7.. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI Press/MIT Press, 1996.

Back to the journal content
Creative Commons License
This article is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License.
Home | Editorial Board | Author info | Archive | Contact
Copyright JACSM 2007-2023