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Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible
Alind Gupta1, Justin J Slater1, Devon Boyne1,2
1Lighthouse Outcomes, Toronto, ON, Canada.
Insights
Bayesian networks (BNs) effectively predict coronary artery disease (CAD) risk, matching machine learning performance while offering better probability calibration. These models enhance personalized diagnosis and treatment by handling uncertainty and incomplete data in healthcare.
Area of Science:
- Computational biology and bioinformatics
- Medical informatics and decision support systems
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality and morbidity.
- Current risk prediction models often lack transparency and struggle with complex causal relationships.
- There is a need for advanced models that can improve personalized diagnosis and therapy selection for CAD.
Purpose of the Study:
- To utilize Bayesian networks (BNs) for modeling and predicting the risk of coronary artery disease (CAD).
- To demonstrate the utility of BNs in incorporating background knowledge, handling missing data, and enabling adaptive decision-making under uncertainty.
- To compare the performance of BNs against traditional machine learning classifiers for CAD risk prediction.
Main Methods:
- Bayesian networks (BNs) were employed to model CAD risk using the Z-Alizadehsani dataset, comprising 303 Iranian patients.
- The study explored BNs' capabilities in managing incomplete observations and facilitating adaptive decision-making.
- Performance was evaluated using the area under the receiver-operating characteristic curve (AUC) via 10-fold cross-validation.
Main Results:
- BNs achieved a mean 10-fold AUC of 0.93 ± 0.04, comparable to logistic regression, SVM, and ANN.
- BNs demonstrated superior probability calibration compared to other machine learning classifiers.
- The study successfully illustrated the application of BNs for prediction with missing data and adaptive prognostic value calculation.
Conclusions:
- Bayesian networks are powerful and versatile tools for risk prediction and health outcomes research.
- BNs complement traditional statistical methods, especially in medical domains with uncertain or incomplete information.
- The interpretability and robustness of BNs make them particularly valuable for personalized CAD diagnosis and treatment decisions.
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.
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