Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study
Open accessAbstract
Background: Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. Methods: We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. Results: Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. Conclusions: Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.
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A study using retrospective cohort data applied the DT algorithm to predict comorbidity of diabetes and CVD, achieving an accuracy of 94.09%.
“a study using retrospective cohort data applied the DT algorithm to predict comorbidity of diabetes and CVD, achieving an accuracy of 94.09% [93].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [93]
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Self-citation
- Authors
- Abdalrada, A. S., Abawajy, J., Al-Quraishi, T., & Islam, S. M. S.
- Affiliations
- Deakin University (AU)
- Corresponding author
- Islam, Sheikh Mohammed Shariful
- Venue
- Journal of Diabetes & Metabolic Disorders
- Published
- 2022-01-12
- Volume
- 21
- Issue
- 1
- Pages
- 251-261
- ISSN
- 2251-6581 (ISSN-L)
- PMC
- PMC9167176
- OpenAlex
- W4205802501
- Cited by
- 78
- References
- 77
- Citations by year
- 2026: 12 · 2025: 16 · 2024: 20 · 2023: 19 · 2022: 11
- FWCI
- 12.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Diabetes & Metabolic Disorders (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Own your potential (DEAKIN) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Deakin University
- 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
- diabetes mellitus, cardiovascular disease, comorbidity prediction, machine learning prediction models, HbA1c, HDL cholesterol, TC/HDL-C ratio, risk factors, diabetes screening, early detection, multivariate adaptive regression splines
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