Artificial Intelligence in Medicine
Data driven generation of fuzzy systems: an application to breast cancer detection
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Expert Systems with Applications: An International Journal
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method
Journal of Medical Systems
Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions
Journal of Medical Systems
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Knowledge-Based Systems
Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis
International Journal of Knowledge Discovery in Bioinformatics
Computer Methods and Programs in Biomedicine
A hybrid intelligent system for medical data classification
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.