Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Exploration and visualization of OLAP cubes with statistical tests
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Evaluating statistical tests on OLAP cubes to compare degree of disease
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Quantitative motor function evaluation: the VAMA project experience
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Evaluating association rules and decision trees to predict multiple target attributes
Intelligent Data Analysis
Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach
Journal of Medical Systems
Interactive exploration and visualization of OLAP cubes
Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
Utility-guided Clustering-based Transaction Data Anonymization
Transactions on Data Privacy
Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions
Journal of Medical Systems
Association rule mining to detect factors which contribute to heart disease in males and females
Expert Systems with Applications: An International Journal
Discovering frequent pattern pairs
Intelligent Data Analysis
Developing a hybrid predictive system for retinopathy
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Association rules represent a promising technique to improve heart disease prediction. Unfortunately, when association rules are applied on a medical data set, they produce an extremely large number of rules. Most of such rules are medically irrelevant and the time required to find them can be impractical. A more important issue is that, in general, association rules are mined on the entire data set without validation on an independent sample. To solve these limitations, we introduce an algorithm that uses search constraints to reduce the number of rules, searches for association rules on a training set, and finally validates them on an independent test set. The medical significance of discovered rules is evaluated with support, confidence, and lift. Association rules are applied on a real data set containing medical records of patients with heart disease. In medical terms, association rules relate heart perfusion measurements and risk factors to the degree of disease in four specific arteries. Search constraints and test set validation significantly reduce the number of association rules and produce a set of rules with high predictive accuracy. We exhibit important rules with high confidence, high lift, or both, that remain valid on the test set on several runs. These rules represent valuable medical knowledge