First IEEE International Conference on Data Mining (ICDM'01)
Mining Constrained Association Rules to Predict Heart Disease
San Jose, California
November 29-December 02
ISBN: 0-7695-1119-8
This work describes our experiences on discovering association rules in medical data to predict heart disease. We focus on two aspects in this work: mapping medical data to a transaction format suitable for mining association rules and identifying useful constraints. Based on these aspects we introduce an improved algorithm to discover constrained association rules. We present an experimental section explaining several interesting discovered rules.
Citation:
Carlos Ordonez, Edward Omiecinski, Levien de Braal, Cesar A. Santana, Norberto Ezquerra, Jose A. Taboada, David Cooke, Elizabeth Krawczynska, Eenest V. Garcia, "Mining Constrained Association Rules to Predict Heart Disease," icdm, pp.433, First IEEE International Conference on Data Mining (ICDM'01), 2001