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Coronary Artery Disease Detection Using a Fuzzy-Boosting PSO Approach
- Source :
- Computational Intelligence and Neuroscience, Vol 2014 (2014), Computational Intelligence and Neuroscience
- Publication Year :
- 2014
- Publisher :
- Hindawi Limited, 2014.
-
Abstract
- In the past decades, medical data mining has become a popular data mining subject. Researchers have proposed several tools and various methodologies for developing effective medical expert systems. Diagnosing heart diseases is one of the important topics and many researchers have tried to develop intelligent medical expert systems to help the physicians. In this paper, we propose the use of PSO algorithm with a boosting approach to extract rules for recognizing the presence or absence of coronary artery disease in a patient. The weight of training examples that are classified properly by the new rules is reduced by a boosting mechanism. Therefore, in the next rule generation cycle, the focus is on those fuzzy rules that account for the currently misclassified or uncovered instances. We have used coronary artery disease data sets taken from University of California Irvine, (UCI), to evaluate our new classification approach. Results show that the proposed method can detect the coronary artery disease with an acceptable accuracy. Also, the discovered rules have significant interpretability as well.
- Subjects :
- Time Factors
Boosting (machine learning)
Article Subject
Databases, Factual
General Computer Science
Computer science
General Mathematics
MEDLINE
Coronary Artery Disease
computer.software_genre
Machine learning
lcsh:Computer applications to medicine. Medical informatics
Sensitivity and Specificity
Fuzzy logic
Statistics, Nonparametric
lcsh:RC321-571
Coronary artery disease
Fuzzy Logic
Artificial Intelligence
medicine
Humans
Diagnosis, Computer-Assisted
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Interpretability
business.industry
General Neuroscience
Nonparametric statistics
Particle swarm optimization
General Medicine
medicine.disease
Expert system
ComputingMethodologies_PATTERNRECOGNITION
lcsh:R858-859.7
Data mining
Artificial intelligence
business
computer
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 16875273 and 16875265
- Volume :
- 2014
- Database :
- OpenAIRE
- Journal :
- Computational Intelligence and Neuroscience
- Accession number :
- edsair.doi.dedup.....7b2f6d6a9c758248d233b251d5794bb2