A fuzzy classification system based on Ant Colony Optimization for diabetes disease diagnosis

Authors:
Mostafa Fathi Ganji;Mohammad Saniee Abadeh
Affiliations:
Faculty of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Iran;Faculty of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Jalal Ale Ahmad Highway, P.O. Box 14115-143, Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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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.