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

• 발행기관: 한국역학회
• 저자명: Shiva Borzouei Ali Reza Soltanian
• 간행물 정보: 『Epidemiology and Health』40, 1~8쪽, 전체 8쪽
• 주제분류: 의약학 > 면역학
• 파일형태: PDF
• 발행일자: 2018.01.01

영문 초록

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.

목차

INTRODUCTION

MATERIALS AND METHODS

RESULTS

DISCUSSION

키워드

Statistical model Glycated hemoglobin A Epidemiology Iran