Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method

Alind Gupta, Justin J. Slater, Devon Boyne, Nicholas Mitsakakis, Audrey Béliveau, Marek J. Druzdzel, Darren R. Brenner, Selena Hussain and Paul Arora
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Alind Gupta: Lighthouse Outcomes, Toronto, ON, Canada
Justin J. Slater: Lighthouse Outcomes, Toronto, ON, Canada
Devon Boyne: Lighthouse Outcomes, Toronto, ON, Canada
Nicholas Mitsakakis: Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
Audrey Béliveau: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
Marek J. Druzdzel: Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland
Darren R. Brenner: Lighthouse Outcomes, Toronto, ON, Canada
Selena Hussain: Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
Paul Arora: Lighthouse Outcomes, Toronto, ON, Canada

Medical Decision Making, 2019, vol. 39, issue 8, 1032-1044

Abstract: Objectives. Coronary artery disease (CAD) is the leading cause of death and disease burden worldwide, causing 1 in 7 deaths in the United States alone. Risk prediction models that can learn the complex causal relationships that give rise to CAD from data, instead of merely predicting the risk of disease, have the potential to improve transparency and efficacy of personalized CAD diagnosis and therapy selection for physicians, patients, and other decision makers. Methods. We use Bayesian networks (BNs) to model the risk of CAD using the Z-Alizadehsani data set—a published real-world observational data set of 303 Iranian patients at risk for CAD. We also describe how BNs can be used for incorporation of background knowledge, individual risk prediction, handling missing observations, and adaptive decision making under uncertainty. Results. BNs performed on par with machine-learning classifiers at predicting CAD and showed better probability calibration. They achieved a mean 10-fold area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.04, which was comparable with the performance of logistic regression with L1 or L2 regularization (AUC: 0.92 ± 0.06), support vector machine (AUC: 0.92 ± 0.06), and artificial neural network (AUC: 0.91 ± 0.05). We describe the use of BNs to predict with missing data and to adaptively calculate prognostic values of individual variables under uncertainty. Conclusion. BNs are powerful and versatile tools for risk prediction and health outcomes research that can complement traditional statistical techniques and are particularly useful in domains in which information is uncertain or incomplete and in which interpretability is important, such as medicine.

Keywords: Bayesian networks; coronary artery disease; graphical models; risk prediction; health economics and outcomes research (HEOR); machine learning; artificial intelligence; risk modeling; cardiology; statistical models; Bayesian statistics (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:39:y:2019:i:8:p:1032-1044

DOI: 10.1177/0272989X19879095

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