Comparison of multilayer perceptron neural network based on wrapper algorithm, discriminant analysis and logistic regression in determining risk factors of type 2 diabetes

The present study aimed to evaluate the performance of three statistical models in predicting diabetes type 2 as well as to identify its risk factors.
The data related to the potential risk factors of body mass index (BMI), fasting blood sugar (FBS), hypertension (HT), lipids (TC, TG, HDL and LDL), HbA1C and smoking history of 300 patients were extracted from medical records. Artificial neural network multi-layer perceptron (MLP), discriminant analysis (DA) and logistic regression (LR) were applied to identify risk factors. ROC curve was used to compare the performance of the models. To fix the problem "over fitting", the MLP model algorithm was used Wrapper.
The prediction power of the MLP, DA and LR based on the ROC curve were 0.984, 0.981and 0.983, respectively. In the LR model, variables FBS (P According to the findings of the present study, the performance of the three used methods of MLP, LR and DA were similar. It is suggested to use the LR where there is a need to simple interpretation as it provides OR for a group relative to the other one while MLP acts like a black box that does not show the relationship between variables. It is also suggested to conduct studies for further investigation of the performance of these methods.