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Can machine-learning improve cardiovascular risk prediction using routine clinical data

Weng, Stephen F.; Reps, Jenna M.; Kai, Joe; Garibaldi, Jonathan M.; Quereshi, Nadeem

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Authors

Stephen F. Weng

Jenna M. Reps

Nadeem Quereshi



Contributors

Bin Liu
Editor

Abstract

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 78 highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity
79 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.

Journal Article Type Article
Acceptance Date Mar 23, 2017
Online Publication Date Apr 4, 2017
Publication Date Apr 4, 2017
Deposit Date Mar 28, 2017
Publicly Available Date Apr 4, 2017
Journal PLOS ONE
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 12
Issue 4
Article Number e0174944
DOI https://doi.org/10.1371/journal.pone.0174944
Public URL https://nottingham-repository.worktribe.com/output/854474
Publisher URL http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174944

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