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Epidemiol Health. 2018;40(1):e2018007. English. Original Article. https://doi.org/10.4178/epih.e2018007
Borzouei S , Soltanian AR .
Department of Endocrinology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. soltanian@umsha.ac.ir
Modeling of Noncommunicable Diseases Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Abstract

OBJECTIVES

To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.

METHODS

This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps.

RESULTS

Variables found to be significant at a level of p < 0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM.

CONCLUSIONS

In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Copyright © 2019. Korean Association of Medical Journal Editors.