PMID- 29529860
OWN - NLM
STAT- MEDLINE
DCOM- 20180423
LR  - 20221207
IS  - 2092-7193 (Electronic)
IS  - 2092-7193 (Linking)
VI  - 40
DP  - 2018
TI  - Application of an artificial neural network model for diagnosing type 2 diabetes 
      mellitus and determining the relative importance of risk factors.
PG  - e2018007
LID - 10.4178/epih.e2018007 [doi]
LID - e2018007
AB  - 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.
FAU - Borzouei, Shiva
AU  - Borzouei S
AUID- ORCID: 0000-0001-6826-9872
AD  - Department of Endocrinology, School of Medicine, Hamadan University of Medical 
      Sciences, Hamadan, Iran.
FAU - Soltanian, Ali Reza
AU  - Soltanian AR
AUID- ORCID: 0000-0002-7483-3502
AD  - Department of Biostatistics, School of Public Health, Hamadan University of 
      Medical Sciences, Hamadan, Iran.
AD  - Modeling of Noncommunicable Diseases Research Center, School of Public Health, 
      Hamadan University of Medical Sciences, Hamadan, Iran.
LA  - eng
PT  - Journal Article
DEP - 20180310
PL  - Korea (South)
TA  - Epidemiol Health
JT  - Epidemiology and health
JID - 101519472
RN  - 0 (Glycated Hemoglobin A)
RN  - 0 (hemoglobin A1c protein, human)
SB  - IM
MH  - Adult
MH  - Age Distribution
MH  - Diabetes Mellitus, Type 2/*diagnosis/*epidemiology
MH  - Female
MH  - Glycated Hemoglobin/metabolism
MH  - Humans
MH  - Iran/epidemiology
MH  - Male
MH  - Mass Screening/methods
MH  - Middle Aged
MH  - *Models, Statistical
MH  - *Neural Networks, Computer
MH  - Risk Assessment
MH  - Risk Factors
MH  - Waist Circumference
PMC - PMC5968209
OTO - NOTNLM
OT  - Epidemiology
OT  - Glycated hemoglobin A
OT  - Iran
OT  - Statistical model
COIS- The authors have no conflicts of interest to declare for this study
EDAT- 2018/03/14 06:00
MHDA- 2018/04/24 06:00
PMCR- 2018/03/10
CRDT- 2018/03/14 06:00
PHST- 2018/02/04 00:00 [received]
PHST- 2018/03/10 00:00 [accepted]
PHST- 2018/03/14 06:00 [pubmed]
PHST- 2018/04/24 06:00 [medline]
PHST- 2018/03/14 06:00 [entrez]
PHST- 2018/03/10 00:00 [pmc-release]
AID - epih.e2018007 [pii]
AID - epih-40-e2018007 [pii]
AID - 10.4178/epih.e2018007 [doi]
PST - epublish
SO  - Epidemiol Health. 2018 Mar 10;40:e2018007. doi: 10.4178/epih.e2018007. 
      eCollection 2018.