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Comparison of multilayer perceptron neural network based on wrapper algorithm, discriminant analysis and logistic regression in determining risk factors of type 2 diabetes
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Eghbal Zandkarimi   , Majid Sadeghifar , Mansour Rezaei |
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Abstract: (5188 Views) |
| Introduction: 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.
Materials and Methods: 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.
Results: 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 |
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| Keywords: Artificial Neural Network MLP, Logistic Regression, Discriminant Analysis, Wrapper, Curve Rock |
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Full-Text [PDF 761 kb]
(1074 Downloads)
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Type of Study: Research |
Subject:
General
Received: 2016/10/4 | Accepted: 2017/04/8 | Published: 2018/01/1
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