A Fuzzy Classifier Based on Modified Particle Swarm Optimization for Diabetes Disease Diagnosis

Hamid Reza Sahebi, Sara Ebrahimi


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. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. In this paper a novel fuzzy classifier for diagnosis of diabetes disease along with feature selection is proposed. The aim of this paper is to use a modified particle swarm optimization algorithm to extract a set of fuzzy rules for diagnosis of diabetes disease. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification accuracy is 85.19% which reveals that proposed method, outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis.


Diabetes disease diagnosis; Particle swarm optimization; Fuzzy classifier

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