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Cited by 40Artificial Intelligence in HealthcareDiabetes, Cardiovascular Risks, and LipoproteinsMachine Learning in Healthcare

Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

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Zhang, Lei, Shang, Xianwen, Sreedharan, Subhashaan, Yan, Xixi, Liu, Jianbin, Keel, Stuart, Wu, Jinrong, Peng, Wei +1 more·2020·JMIR Medical Informatics
DOI 10.2196/16850PMC PMC7420582

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Abstract

Background: Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. Objective: We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. Methods: We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. Results: Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001). Conclusions: A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.

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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 [46]

    The Results (Participant Characteristics; no page number in our copy) associate later diabetes with overweight or obesity, self-reported hypertension and dyslipidemia, smoking, less physical activity, and some dietary measures. However, the machine-learning Results explicitly identify BMI as a leading predictor; they do not establish that every listed factor was identified by the models as increasing risk. The observational associations also do not establish that those factors cause diabetes. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Zhang, L., Shang, X., Sreedharan, S., Yan, X., Liu, J., Keel, S., Wu, J., Peng, W., & He, M.
Affiliations
  • Xi'an Jiaotong University (CN)
  • The University of Melbourne (AU)
  • La Trobe University (AU)
Corresponding author
Zhang, Lei
ORCID
Venue
JMIR Medical Informatics
Publisher
JMIR Publications Inc.
Published
2020-07-28
Volume
8
Issue
7
Pages
e16850
ISSN
2291-9694 (ISSN-L)
DOI
10.2196/16850
PMC
PMC7420582
OpenAlex
W3008968780
Cited by
40
References
1
Citations by year
2026: 3 · 2025: 5 · 2024: 4 · 2023: 8 · 2022: 6 · 2021: 11 · 2020: 3
FWCI
6.15
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Informatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Heart Foundation of Australia
  • University of Melbourne
  • National Natural Science Foundation of China (xzy032020032, YX6J004, xtr022019003, 20200344, 81950410639, 3111500001, 81420108008)
  • NSW Ministry of Health
  • Cancer Council NSW
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 › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
type 2 diabetes, diabetes risk prediction, machine learning, gradient boosting, body mass index, obesity, sex differences, 45 and Up Study, longitudinal cohort, diabetes incidence, weight loss interventions, risk prediction model comparison, health workforce planning

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