Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review
Open accessAbstract
People with type 2 diabetes have a higher risk of cardiovascular disease morbidity and mortality. We aim to distil the evidence, summarize the developments, and identify the gaps in relevant research on predicting cardiovascular disease in type 2 diabetes people using AI techniques in the last ten years. A systematic search was carried out for literature published between 1st January 2010 and 30th May 2021 in five medical and scientific databases, including Medline, EMBASE, Global Health (CABI), IEEE Xplore and Web of Science Core Collection. All English language studies describing AI models for predicting cardiovascular diseases in adults with type 2 diabetes were included. The retrieved studies were screened and the data from included studies were extracted by two reviewers. The survey and synthesis of extracted data were conducted based on predefined research questions. IJMEDI checklist was used for quality assessment. From 176 articles identified by the search, 5 studies with sample sizes ranging from 560 to 203,517 met our inclusion criteria. The models predicted the risk of multiple cardiovascular diseases over 5 or 10 years. Ensemble learning, particularly random forest, is the most used algorithm in these models and consistently provided competitive performance. Commonly used features include age, body mass index, blood pressure measurements, and cholesterol measurements. Only one study carried out external validation. The area under the receiver operating characteris…
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Machine learning models can improve discrimination or reclassification in derivation datasets, especially when longitudinal or high-dimensional EHR features are available.
“Rather than focusing on isolated “best-case” results, the more consistent message across the literature is that machine learning models can improve discrimination or reclassification in derivation datasets, especially when longitudinal or high-dimensional EHR features are available (8, 11–13).”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [11]
The Abstract (p. 1) reports that AI models outperformed conventional models in some scenarios and gives AUROC values for derivation cohorts. Table 2 (p. 5) lists one study with thousands of hospital diagnoses, procedures and prescriptions. The review does not establish that improvements are especially associated with high-dimensional or longitudinal EHR features, or report improved reclassification. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe core strengths of machine learning in cardiovascular risk prediction lie in its capacity to handle complex, high-dimensional data and to adapt effectively to class imbalance, enabling the identification of non-linear risk patterns that traditional models often fail to capture.
“The core strengths of machine learning in cardiovascular risk prediction lie in its capacity to handle complex, high-dimensional data and to adapt effectively to class imbalance, enabling the identification of non-linear risk patterns that traditional models often fail to capture (11).”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [11]
The Model development summary (p. 2) reports that three reviewed studies addressed class imbalance using class weights, sub-sampling or ensemble modelling. The Model performance summary (p. 3) reports that machine-learning models outperformed regression models for several outcomes, though the differences were small. The paper does not establish that handling high-dimensional data or detecting non-linear patterns explains that performance, and it does not show effective adaptation to imbalance across all the models. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMachine learning can autonomously discern non-linear risk patterns from high-dimensional data, iteratively train and validate across algorithms, and identify optimal performers through comparative evaluation.
“Unlike traditional regression models reliant on fixed assumptions and linear frameworks, machine learning can autonomously discern non-linear risk patterns from high-dimensional data, iteratively train and validate across algorithms, and identify optimal performers through comparative evaluation.”
From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [11]
The Model development summary (p. 2) describes data-driven machine-learning models and hyperparameter tuning, while the Model performance summary (p. 3) compares algorithms using AUROC and identifies the best-performing models. The paper does not establish that these models autonomously discerned non-linear patterns from high-dimensional data or that they were iteratively trained and validated across algorithms; it reports that only three studies described data splits and tuning, and only one conducted external validation. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Cites
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Sorted by citations
Huang, Q., Zou, X., Lian, Z., Zhou, X., Han, X., Luo, Y., Chen, S., Wang, Y., Wu, S., & Ji, L. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02611-0 · Cited by 15 · see citing works
Li, H., Xu, Z., Cen, Y., & Liu, X. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1831220
- Authors
- Wang, M., Francis, F., Kunz, H., Zhang, X., Wan, C., Liu, Y., Taylor, P., Wild, S. H., & Wu, H.
- Affiliations
- University College London (GB)
- University of Edinburgh (GB)
- Southeast University (CN)
- Nanjing Medical University (CN)
- Corresponding author
- Wang, Minhong
- ORCID
- Venue
- Intelligence-Based Medicine
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 6
- Pages
- 100072
- ISSN
- 2666-5212 (ISSN-L)
- OpenAlex
- W4292858653
- Cited by
- 15
- References
- 6
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 2 · 2023: 2
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Update notices
- erratum (2025-01-01)
- Open access
- gold
- doi.org
- Licence
- Locations
- Intelligence-Based Medicine (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Medical Research Council (MR/S004149/1)
- Health Data Research UK (MR/S004149/2)
- British Council
- University of Edinburgh
- University College London
- National Institute for Health and Care Research (NIHR202639)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- type 2 diabetes, cardiovascular disease prediction, artificial intelligence, random forest, ensemble learning, cardiovascular risk score, external validation, reproducibility, risk prediction model
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