Comparing Association Rules and Decision Trees for Disease Prediction
Association rules represent a promising technique to find hidden patterns in a medical data set. The main issue about mining association rules in a medical data set is the large number of rules that are discovered, most of which are irrelevant. Such number of rules makes search slow and interpretation by the domain expert difficult. In this work, search constraints are introduced to find only medically significant association rules and make search more efficient. In medical terms, association rules relate heart perfusion measurements and patient risk factors to the degree of stenosis in four specific arteries.