Authors:
Luis Zárate
;
Paulo Alvarenga
;
Romero Paoliello
and
Thiago Ribeiro
Affiliation:
Applied Computational Intelligence Laboratory (LICAP), Pontifical Catholic University of Minas Gerais (PUC), Brazil
Keyword(s):
KDD, Data Mining, Discriminant Rules, Clinical Databases, Nephrolithiasis
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Nephrolithiasis is a disease that is unknown yet a clinical treatment that determines its cure. In the adult population is esteemed an incidence around 5 to 12%, being a little lesser in the pediatric band. The renal colic, caused by nephrolithiasis, is the main disease symptom in the adults and it is observed in 14% of the pediatric patients. The disease symptoms in the pediatric patient don't follow a pattern, and this makes difficult the disease diagnosis. The main objective of this work is discovery the patters of the disease symptoms and identifies the population apt to acquire it. With this objective, is applied KDD methodology determining discriminant rules for the patterns of the symptoms, and with this, select the groups of patients with those sets of symptoms. Finally, the results and the conclusions of the work are presented.