Article ID Journal Published Year Pages File Type
385087 Expert Systems with Applications 2011 10 Pages PDF
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.

► This paper is the first comprehensive proposal for utilizing ACO for Fuzzy Rule Discovery from diabetes disease data. ► FCS-ANTMINER has new features that make it different from existing ACO-Based classification systems. ► The accuracy of FCS-ANTMINER is 84.24% for diabetes disease data which is superior to several famous and recent classifiers. ► FCS-ANTMINER produces a few short fuzzy rules which reveal the interpretability of the resulted classification system.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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