Volume 11, Number 2 (Vol 11, No 2 2015)                   irje 2015, 11(2): 46-53 | Back to browse issues page


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Jahani M, Rezaenoor J, Hadavandi E, Salehi I, Tahsini H. Comparison of Decision Support Systems for Diabetes Prediction. irje. 2015; 11 (2) :46-53
URL: http://irje.tums.ac.ir/article-1-5390-en.html

1- Department of Information Technology, University of Qom, Qom, Iran
2- Department of Information Technology, University of Qom, Qom, Iran , j.rezaee@qom.ac.ir
3- Department of Textile Engineering, Amirabad University of Technology, Tehran
4- Department of Biostatistics & Epidemiology, University Hamadan of Medical Sciences, Hamadan,Iran
5- Department of Physiology; Hamadan University of Medical Sciences, Hamadan, Iran
Abstract:   (4826 Views)

Background & Objectives: In recent years, different decision support systems (DSS) have been used to predict and diagnose diseases. The purpose of this paper was to compare some DSSs and to evaluate their accuracy in predicting diabetes. 

Methods: In this research, determination and optimization of the weights of the neural network were undertaken using genetic algorithm and Levenberg-Marquardt (GALM). Traditional and K-Fold Cross Validation were used to verify the models. Finally, the proposed model (i.e. GALM) was compared using logistic regression and genetic algorithm based on area under curve (AUC), and Confusion Matrix.

Results: After evaluating the results, the model based on the GALM algorithm showed better sensitivity and specificity in comparison with models based on the logistic regression (LR) and genetic algorithm (GA). Furthermore, among other models, the proposed model had a high sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and a small negative likelihood.

Conclusion: The results showed that the GALM model with a sensitivity, specificity, PPV, NPV, and AUC of 98.7, 90.01, 91.8, 98.3 and 0.979 respectively was an appropriate model for predicting diabetes in comparison with models of GA and LR.

Full-Text [PDF 573 kb]   (1349 Downloads)    
Type of Study: Research | Subject: General
Received: 2015/11/22 | Accepted: 2015/11/22 | Published: 2015/11/22

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