epiH Search


Epidemiol Health > Accepted Articles
Epidemiology and Health 2018;e2018007.
DOI: https://doi.org/10.4178/epih.e2018007    [Accepted] Published online March 10, 2018.
Application of Artificial Neural Network Model for Diagnosis of Type 2 Diabetes Mellitus and Determine the Importance Degree of Risk Factors, Iran
Shiva Borzouei  , Ali Reza Soltanian 
Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medi, Hamadan, Iran
Correspondence  Ali Reza Soltanian ,Tel: +988138380025, Fax: +988138380509, Email: soltanian@umsha.ac.ir
Submitted: February 4, 2018  Accepted after revision: March 10, 2018
Identify the most important of demographic risk factors to the diagnosis of Type 2 Diabetes Mellitus (T2DM) using the neural network model
In this study was conducted on 234 samples, and data were collected from individuals referring to diabetes center in Hamadan city (west of Iran) from 27 November to 15 March 2016. Diagnosis of normal people and diabetics was performed by HbA1c measures. Multilayer perceptron artificial neural network used to identify demographic risk factors on T2DM and their importance rate. DeLong's method was used to compare the models fitting in sequential steps.
Using univariate logistic regression risk factors age, hypertension, waist, BMI, sedentary, smoke, vegetables, family history of T2DM, stress, walking, fruit and sex which had a significant level of less than 0.2, entered the model at first. After seven stages of neural network modeling, only age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoke (49.3%) and family history of T2DM (37.2%), respectively, were identified to the diagnosis of T2DM.
In this study waist and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that they are used as a proper tool for risk assessment of type 2 diabetes in screening tests.
Keywords: Statistical model; Glycated Hemoglobin A; Epidemiology
Share :
METRICS Graph View
  • 0 Crossref
  • 0 Scopus
  • 62 View
  • 13 Download


Browse all articles >

Editorial Office
Graduate School of Cancer Science and Policy, National Cancer Center
323 Ilsan-ro, Ilsandong-gu, Goyang 10408, Korea
TEL: +82-2-745-0662   FAX: +82-2-764-8328    E-mail: enh0662@gmail.com

Copyright © 2018 by Korean Society of Epidemiology. All rights reserved.

Developed in M2community

Close layer
prev next