|Detection of Functional Imitation in Diabetic Patients Based on The Optimal Combination of Care Indicators By Using Ramp AUC and Comparison Its Performance With Existing Methods|
|Parvin Sarbakhsh1, Leili Faraj2, Mohamad Asghari Jafarabadi1, Seyed Morteza Shamshirgaran3|
|1Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
2Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
3Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
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Keywords : Ramp AUC model, SVM, GAM, Diabetes, Functional limitation, Classification, Kernel function, RBF kernel
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Objective: The area under a ROC curve (AUC) is a common criterion to assess the overall classification performance of the markers. In practice due to limited classification ability of a single marker, we are interested in combining markers linearly or nonlinearly to improve classification performance. Ramp AUC (RAUC) is a new statistical AUC-based method which can find such optimal combinations of markers. In this study, RAUC was used to find the optimal combinations of care indicators related to functional limitation as a complication of diabetes and accurately discriminate this outcome based on its underlying markers.
Materials and methods: This cross-sectional study was conducted on 378 diabetic patients referred to diabetic centers of Ardebil and Tabriz during 2014–15. To have an accurate classification of diabetic patients according to their functional limitation status, RAUC method with RBF kernel was employed to look for an optimal combination of care indicators. Classification performance of the model was evaluated by AUC and compared with logistic regression, support vector machine (SVM) and generalized additive model (GAM) via training and test validation method.
Results: Out of 378 diabetics, 67.46% had functional limitation. RAUC had a test dataset AUC equal 1 and outperformed logistic (AUC=.79), GAM (AUC=.82), SVM with linear kernel (AUC=.67) and was slightly better than SVM with RBF kernel (AUC=.98).
Conclusion: There was strong non linearity in data and RAUC with RBF kernel which is a nonlinear combination of markers could detect this pattern
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