Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining and knowledge discovery in databases
Communications of the ACM
Mining risk patterns in medical data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Using classification trees to assess low birth weight outcomes
Artificial Intelligence in Medicine
Analysis of breast feeding data using data mining methods
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Return on investment for business intelligence
MCBE'07 Proceedings of the 8th Conference on 8th WSEAS Int. Conference on Mathematics and Computers in Business and Economics - Volume 8
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
Business intelligence provides organizations with the ability to maintain a competitive advantage in the market. This property can be used in wide fields such as health care where lower costs and early prevention of patients are the main goals. This article develops an approach to the use of business intelligence to achieve a centralized clinical data and apply data mining, particularly the use of association rules. All this in order to find risk factors associated with diabetes mellitus type 2 (DM2) and the way healthcare providers perform the management of this disease. The experiment was conducted using a database of patients with DM2 treated by a health care provider entity in Colombia.