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 Drużdżel , Darren R. Brenner , Selena Hussain , Paul Arora


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.
Author Alind Gupta
Alind Gupta,,
, Justin J. Slater
Justin J. Slater,,
, Devon Boyne
Devon Boyne,,
, Nicholas Mitsakakis
Nicholas Mitsakakis,,
, Audrey Béliveau
Audrey Béliveau,,
, Marek Drużdżel (FCS / SD)
Marek Drużdżel,,
- Software Department
, Darren R. Brenner
Darren R. Brenner,,
, Selena Hussain
Selena Hussain,,
, Paul Arora
Paul Arora,,
Journal seriesMedical Decision Making, ISSN 0272-989X, e-ISSN 1552-681X, (N/A 100 pkt)
Issue year2019
Publication size in sheets0.6
Keywords in EnglishBayesian 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
ASJC Classification2719 Health Policy
Internal identifierROC 19-20
Languageen angielski
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 12-02-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.421; WoS Impact Factor: 2018 = 2.793 (2) - 2018=3.649 (5)
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