Disordered metabolic evaluation in renal stone recurrence : a data mining approach

Saraee, MH ORCID: https://orcid.org/0000-0002-3283-1912, Givchi, A, Taghi Adl, S and Eshraghi, A 2011, 'Disordered metabolic evaluation in renal stone recurrence : a data mining approach' , Applied Computer Science & Mathematics, 5 (11) , pp. 64-68.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Applied Computer Science & Mathematics
Publisher: Stefan cel Mare University of Suceava
ISSN: 2066-4273
Related URLs:
Depositing User: Prof. Mo Saraee
Date Deposited: 12 Jul 2017 08:12
Last Modified: 27 Aug 2021 23:34
URI: http://usir.salford.ac.uk/id/eprint/42929

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