Citation :

Sainabou Ngack, "Bayesian and Stochastic Modeling Approaches to Coronary Artery Disease Progression: A Systematic Review of Methods, Applications, and Clinical Implications," International Journal of Computer Trends and Technology (IJCTT), vol. 74, no. 4, pp. 29-44, 2026. Crossref, https://doi.org/10.14445/22312803/IJCTT-V74I4P103

Abstract

Bayesian and stochastic models in coronary artery disease extend beyond prediction to clinical implications, including adaptive trial designs, personalized medicine, and enhanced risk stratification. Using a systematic review of literature approach, this study examines Bayesian and stochastic modeling approaches to coronary artery disease progression. The study selects literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework, which led to the selection of 17 articles from five databases using some specified inclusion and exclusion criteria. Results show that Bayesian modeling approaches were more prevalent in the predictive landscape of Coronary Artery Disease (CAD), especially when it concerns risk estimation and prognostic modeling. Findings show that while Bayesian techniques focus on probabilistic inference and statistical learning, stochastic techniques prioritize time evolution, physical simulation, and biological plausibility. Results show that Bayesian models perform in individualized risk prediction and calibration, whereas stochastic approaches have strength in providing a deeper understanding of disease dynamics and longitudinal progression patterns. Findings show that Bayesian applications are more patient-specific, while stochastic models support longitudinal and cohort-level management. Results show that Bayesian models are more mature for deployment in predictive clinical environments, while stochastic models provide insight into disease evolution and mechanistic underpinnings. The study establishes that there is a difference between the predictive strength of Bayesian models and the mechanistic interoperability of stochastic models, highlighting the need for integrative modeling frameworks.

Keywords

Bayesian models, Cardiovascular disease, Coronary Artery Disease(CAD), Stochastic models, Applications.

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