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Information Journal Paper

Title

Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes

Pages

  303-311

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.

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    APA: Copy

    ESMAEILY, HABIBOLLAH, TAYEFI, MARYAM, GHAYOUR MOBARHAN, MAJID, & Amirabadizadeh, Alireza. (2018). Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes. IRANIAN BIOMEDICAL JOURNAL, 22(5), 303-311. SID. https://sid.ir/paper/756403/en

    Vancouver: Copy

    ESMAEILY HABIBOLLAH, TAYEFI MARYAM, GHAYOUR MOBARHAN MAJID, Amirabadizadeh Alireza. Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes. IRANIAN BIOMEDICAL JOURNAL[Internet]. 2018;22(5):303-311. Available from: https://sid.ir/paper/756403/en

    IEEE: Copy

    HABIBOLLAH ESMAEILY, MARYAM TAYEFI, MAJID GHAYOUR MOBARHAN, and Alireza Amirabadizadeh, “Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes,” IRANIAN BIOMEDICAL JOURNAL, vol. 22, no. 5, pp. 303–311, 2018, [Online]. Available: https://sid.ir/paper/756403/en

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