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A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis
- Source :
-
Expert Systems with Applications . Nov2011, Vol. 38 Issue 12, p14650-14659. 10p. - Publication Year :
- 2011
-
Abstract
- Abstract: Classification systems have been widely utilized in medical domain to explore patient’s data and extract a predictive model. This model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. The aim of this paper is to use an Ant Colony-based classification system to extract a set of fuzzy rules for diagnosis of diabetes disease, named FCS-ANTMINER. We will review some recent methods and describe a new and efficient approach that leads us to considerable results for diabetes disease classification problem. FCS-ANTMINER has new characteristics that make it different from the existing methods that have utilized the Ant Colony Optimization (ACO) for classification tasks. The obtained classification accuracy is 84.24% which reveals that FCS-ANTMINER outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 38
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 64484856
- Full Text :
- https://doi.org/10.1016/j.eswa.2011.05.018