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

By S. Weng, Jenna Marie Reps, J. Kai, J. Garibaldi, N. Qureshi. PLoS ONE, 2017

1,188 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/5b77625b30ab2fa8abf5c152831a6985a61516ee

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Citations in the corpus

Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “For instance, the case of cardiovascular risk prediction using different ML algorithms with clinical data is studied in [66] and the study concluded that ML techniques improved the prediction efficacy.”
A Review of Challenges and Opportunities in Machine Learning for Health. (2018, background): “For example, inferring drug-resistance status in tuberculosis from whole-genome sequencing data 103 , predicting cardiovascular risk from EHR data 64,104 , readmission and mortality risk from longitudinal EHR data 39,105 , and predicting cardiovascular risk from retinal images 106 .”
Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, background): “A variety of machine learning algorithms have been used to understand the complexity and non-linear interaction between different factors by decreasing the error in prediction and factual outcomes [13].”
Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, background): “4,5 Altogether, the potential for ML to fundamentally change the way we practice medicine is now well-appreciated.”
eDoctor: machine learning and the future of medicine (2018, background): “…Heart Association (ACC/ AHA) guideline’s knowledge-based algorithm and found that not only was the algorithm superior, it also identiﬁed signiﬁcant clinical variables that were not included in other predictive scores, such as chronic obstructive pulmonary disease and severe mental illness [39].”
Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants (2019, methodology): “The importance of model selection and hyper-parameter optimization have been overlooked in previous clinical studies that applied ML in prognostic modeling [16–18].”
A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “Most of these algorithms are not only good at discovering explicit relations among data but also the latent and hidden features that are very difficult to detect by human specialists [176].”