Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Open accessAbstract
BACKGROUND: Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. METHODS: Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). FINDINGS: 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. CONCLUSIONS: Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.
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AI-powered systems have been shown to produce overall lower error rates than traditional methods.
“And, indeed, AI-powered systems have been shown to produce overall lower error rates than traditional methods [19–21].”
From Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020) · cites this paper as [19]
ML models integrating clinical, genetic, and lifestyle factors have been developed to predict an individual's risk of developing cardiovascular disease.
“Researchers have developed ML models that integrate clinical, genetic, and lifestyle factors to predict an individual’s risk of developing cardiovascular disease [4–7].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [5]
AI has been applied in various domains of healthcare including cancer research, cardiology, diabetes, mental health, identification of prognosis, Alzheimer's disease, identification of difference in clinical groups, cardiovascular disease, and stroke-related studies.
“AI has been put in application in various domains of healthcare [83–87] which includes cancer research [88], cardiology [89], diabetes [90], mental health [91], identification of prognosis [92], identification of Alzheimer's disease [93], identification of difference in the clinical groups [94], identification of cardiovascular disease [39], stroke-related studies [95], etc.”
From Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective (2020) · cites this paper as [89]
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- Authors
- Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N.
- Affiliations
- University of Nottingham (GB)
- Corresponding author
- Weng, Stephen F.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-04-04
- Volume
- 12
- Issue
- 4
- Pages
- e0174944
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5380334
- OpenAlex
- W2605253636
- Cited by
- 1,346
- References
- 48
- Citations by year
- 2026: 128 · 2025: 162 · 2024: 160 · 2023: 172 · 2022: 174 · 2021: 198 · 2020: 163 · 2019: 104 · 2018: 71 · 2017: 13
- FWCI
- 131.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Funders
- National Institute for Health Research (NIHR School for Primary Care Research Fellowship (2015-2018))
- 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 › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Keywords
- cardiovascular risk prediction, machine learning, random forest, gradient boosting, artificial neural networks, logistic regression, statin initiation, prophylaxis, routine clinical data, primary care, American College of Cardiology guidelines, risk prediction accuracy
- MeSH terms
- Machine Learning, Adult, Aged, Aged, 80 and over, Algorithms, Cardiovascular Diseases, Female, Humans, Male, Middle Aged, Prospective Studies, Risk Factors, Cohort Studies, Logistic Models, Neural Networks, Computer, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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