Epidemiol Health.  2018;40:e2018007. 10.4178/epih.e2018007.

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors

• Borzouei S
• Soltanian

• KMID: 2438462
• DOI: http://doi.org/10.4178/epih.e2018007

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.

Keyword

Statistical model; Glycated hemoglobin A; Epidemiology; Iran

MeSH Terms

Body Mass Index
Diabetes Mellitus, Type 2*
Diagnosis
Epidemiology
Fruit
Humans
Hypertension
Iran
Logistic Models
Mass Screening
Methods
Models, Statistical
Neural Networks (Computer)*
Risk Assessment
Risk Factors*
Sedentary Lifestyle
Sensitivity and Specificity
Smoke
Smoking
Vegetables
Waist Circumference
Walking
Smoke