Assessing association rules and decision trees on analysis of diabetes data from the DiabCare program in France.
Recent advances in information technology have made it possible to solve increasingly complex problems, and also to collect and store huge amounts of information. These vast quantities of data further have to be transformed into relevant value-added and "decision-quality" knowledge. It is against this background that the KDD (Knowledge Discovery in Databases), a multidisciplinary field using computer learning, artificial intelligence, statistics, database technology, expert systems, and data visualization, appeared in the early 90's. In order to assess these technologies in the medical field, we have tested some of these techniques on a large database at our disposal, named DiabCare stemming from the WHO - DiabCare program for the application of the Saint-Vincent Declaration. It contains evaluation data on the health care of patients with diabetes, and in particular, its complications. So far, data analysis has been done using classical statistical methods, and we now intend to make use of such data-mining tools as Associations Rules and Decision and Classification Trees for further exploration of this database. The results presented here show that data mining techniques can be used successfully to extract knowledge from medical databases. The results obtained using Association Rules and especially Decision Trees are very promising.
CERIM, Faculté de Mèdecine-1, Place de Verdun, 59045 Lille, France. jquentin@univ-lille2.fr
Julie
J
Quentin-Trautvetter
Patrick
P
Devos
Alain
A
Duhamel
Régis
R
Beuscart
D000465:Algorithms; D003663:Decision Trees; D003920:Diabetes Mellitus / Q000628:therapy; D005240:Feasibility Studies; D005602:France; D006801:Humans