:: Volume 22, Issue 5 (9-2018) ::
IBJ 2018, 22(5): 303-311 Back to browse issues page
Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes
Habibollah Esmaeily , Maryam Tayefi , Majid Ghayour-Mobarhan , Alireza Amirabadizadeh
Abstract:  
Background: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and health administrators. The current study aimed at developing and comparing some statistical models to identify the risk factors associated with type 2 diabetes. In this light, artificial neural network (ANN), support vector machines (SVMs), and multiple logistic regression (MLR) models were applied, using demographic, anthropometric, and biochemical characteristics, on a sample of 9528 individuals from Mashhad City in Iran. Methods: This study has randomly selected 6654 (70%) cases for training and reserved the remaining 2874 (30%) cases for testing. The three methods were compared with the help of ROC curve. Results: The prevalence rate of type 2 diabetes was 14% in our population. The ANN model had 78.7% accuracy, 63.1% sensitivity, and 81.2% specificity. Also, the values of these three parameters were 76.8%, 64.5%, and 78.9%, for SVM and 77.7%, 60.1%, and 80.5% for MLR. The area under the ROC curve was 0.71 for ANN, 0.73 for SVM, and 0.70 for MLR. Conclusion: Our findings showed that ANN performs better than the two models (SVM and MLR) and can be used effectively to identify the associated risk factors of type 2 diabetes.
Keywords: Support vector machine, Data mining, Diabetes type 2
Full-Text [PDF 764 kb]      
Type of Study: Full Length | Subject: Enzymology and Protein Chemistry
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Esmaeily H, Tayefi M, Ghayour-Mobarhan M, Amirabadizadeh A. Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes . IBJ. 2018; 22 (5) :303-311
URL: http://ibj.pasteur.ac.ir/article-1-2311-en.html


Volume 22, Issue 5 (9-2018) Back to browse issues page
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