Employing Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Comprehensive Review

Background: Bayesian Networks (BNs) are probabilistic graphical models that leverage Bayes' theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing the integration of medical knowledge into models and addressing uncertainty in a probabilistic manner. Objectives: This review aims to provide an exhaustive overview of the current state of Bayesian Networks in disease diagnosis and prognosis. Additionally, it seeks to introduce readers to the fundamental methodology of BNs, emphasizing their versatility and applicability across varied medical domains. Methods: Employing a meticulous search strategy with MeSH descriptors in diverse scientific databases, we identified 190 relevant references. These were subjected to a rigorous analysis, resulting in the retention of 60 papers for an in-depth review. The robustness of our approach minimizes the risk of selection bias. Results: The selected studies encompass a wide range of medical areas, providing insights into the statistical methodology, implementation feasibility, and predictive accuracy of BNs, as evidenced by an average AUC exceeding 75%. The comprehensive analysis underscores the adaptability and efficacy of Bayesian Networks in diverse clinical scenarios. Conclusion: The encompassing exploration of Bayesian Networks presented in this review highlights their significance and growing impact in the realm of disease diagnosis and prognosis. It underscores the need for further research and development to optimize their capabilities and broaden their applicability in addressing diverse and intricate healthcare challenges.