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Cited by 30Artificial Intelligence in HealthcareDiabetes, Cardiovascular Risks, and LipoproteinsStatistical Methods in Epidemiology

Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression

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Gholipour, Kamal, Asghari-Jafarabadi, Mohammad, Iezadi, Shabnam, Jannati, Ali, Keshavarz, Sina·2018·Eastern Mediterranean Health Journal
DOI 10.26719/emhj.18.012
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Abstract

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a metabolic disease with complex causes, manifestations, complications and management. Understanding the wide range of risk factors for T2DM can facilitate diagnosis, proper classification and cost-effective management of the disease. AIMS: To compare the power of an artificial neural network (ANN) and logistic regression in identifying T2DM risk factors. METHODS: This descriptive and analytical study was conducted in 2013. The study samples were all residents aged 15-64 years of rural and urban areas in East Azerbaijan, Islamic Republic of Iran, who consented to participate (n = 990). The latest data available were collected from the Noncommunicable Disease Surveillance System of East Azerbaijan Province (2007). Data were analysed using SPSS version 19. RESULTS: Based on multiple logistic regression, age, family history of T2DM and residence were the most important risk factors for T2DM. Based on ANN, age, body mass index and current smoking were most important. To test for generalization, ANN and logistic regression were evaluated using the area under the receiver operating characteristic curve (AUC). The AUC was 0.726 (SE = 0.025) and 0.717 (SE = 0.026) for logistic regression and ANN, respectively (P < 0.001). CONCLUSIONS: The logistic regression model is better than ANN and it is clinically more comprehensible.

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  • Modifiable factors identified using AI method increase the risk of developing diabetes, including high blood pressure, high blood cholesterol, tobacco smoking, insufficient physical activity, poor diet, and overweight or obesity.

    “Modifiable factors identified using the AI method increase the risk of developing diabetes, including high blood pressure, high blood cholesterol, tobacco smoking, insufficient physical activity, poor diet, and overweight or obesity.44,45,46”

    From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [45]

    The Results (page unavailable) list BMI, current smoking, controlled and uncontrolled hypertension, and raised total cholesterol as predictors ranked by the artificial neural network. They do not list insufficient physical activity or poor diet as ANN-identified predictors. The paper evaluates prediction of existing type 2 diabetes, not whether each listed factor causes an increased risk of developing it. (LLM, conf=0.90)

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Authors
Gholipour, K., Asghari-Jafarabadi, M., Iezadi, S., Jannati, A., & Keshavarz, S.
Affiliations
  • Tabriz University of Medical Sciences (IR)
  • University of Social Welfare and Rehabilitation Sciences (IR)
Corresponding author
Iezadi, Shabnam
ORCID
Iezadi, Shabnam
Venue
Eastern Mediterranean Health Journal
Publisher
World Health Organization Regional Office for the Eastern Mediterranean (WHO/EMRO)
Published
2018-08-01
Volume
24
Issue
08
Pages
770-777
ISSN
  • 1020-3397 (ISSN-L)
  • 1687-1634
DOI
10.26719/emhj.18.012
OpenAlex
W2891066988
Cited by
30
References
2
Citations by year
2026: 1 · 2025: 7 · 2024: 8 · 2023: 6 · 2022: 1 · 2021: 4 · 2020: 2 · 2019: 1
FWCI
2.21
Type
journal article
Language
en
Open access
Locations
  • Eastern Mediterranean Health Journal (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Tabriz University of Medical Sciences
  • University of Tabriz
  • Student Research Committee, Tabriz University of Medical Sciences
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
type 2 diabetes, diabetes risk factors, artificial neural networks, logistic regression, body mass index, smoking, family history of diabetes, age, East Azarbaijan
MeSH terms
Adolescent, Adult, Age Factors, Diabetes Mellitus, Type 2, Female, Humans, Iran, Male, Medical History Taking, Middle Aged, Risk Factors, ROC Curve, Smoking, Body Mass Index, Prevalence, Logistic Models, Neural Networks, Computer, Young Adult

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