Medical diagnosis using a probabilistic causal network
Applied Artificial Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
ACM SIGKDD Explorations Newsletter
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Constrained Association Rules to Predict Heart Disease
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The Representative Basis for Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Bases for Association Rules Using Closed Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Reducing borders of k-disjunction free representations of frequent patterns
Proceedings of the 2004 ACM symposium on Applied computing
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Mining association rules with improved semantics in medical databases
Artificial Intelligence in Medicine
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Assessment of the risk factors of coronary heart events based on data mining with decision trees
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
A contextual data mining approach toward assisting the treatment of anxiety disorders
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Evaluating association rules and decision trees to predict multiple target attributes
Intelligent Data Analysis
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Knowledge-Based Systems
Using semantic-based association rule mining for improving clinical text retrieval
HIS'13 Proceedings of the second international conference on Health Information Science
Correlating medical-dependent query features with image retrieval models using association rules
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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. Association rule medical significance is evaluated with the usual support and confidence metrics, but also lift. Association rules are compared to predictive rules mined with decision trees, a well-known machine learning technique. Decision trees are shown to be not as adequate for artery disease prediction as association rules. Experiments show decision trees tend to find few simple rules, most rules have somewhat low reliability, most attribute splits are different from medically common splits, and most rules refer to very small sets of patients. In contrast, association rules generally include simpler predictive rules, they work well with user-binned attributes, rule reliability is higher and rules generally refer to larger sets of patients.