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, emphasising their versatility and applicability across varied medical domains. 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 in-depth review. The robustness of our approach minimised 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 area under the curve (AUC) exceeding 75%. The comprehensive analysis underscores the adaptability and efficacy of Bayesian networks in diverse clinical scenarios. The majority of the examined studies demonstrate the potential of BNs as reliable adjuncts to clinical decision-making. The findings of this review affirm the role of Bayesian networks as accessible and versatile artificial intelligence tools in healthcare. They offer a viable solution to address complex medical challenges, facilitating timely and informed decision-making under conditions of uncertainty. The extensive 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 optimise their capabilities and broaden their applicability in addressing diverse and intricate healthcare challenges.

This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys’ packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the first instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging.

Municipal social services remain among the least examined public functions in the local-government efficiency literature, owing to the scarcity of structured administrative data and the difficulty of measuring their outputs. Addressing that gap, this study provides the first Data Envelopment Analysis (DEA)-based efficiency assessment of municipal primary social-care departments in the Valencian Community (Spain), drawing on a purpose-built dataset that combines 2023 administrative survey, budgetary, and socio-demographic information. Its methodological contribution is an integrated evaluation architecture articulating well-established components—input-oriented DEA under variable and constant returns to scale, the Banker–Morey treatment of non-discretionary contextual variables across an exhaustive lattice of specifications, and a super-efficiency ranking—into a single design that disentangles managerial, scale, and environmental sources of inefficiency. Applied to 18 municipalities, the analysis detects substantial technical inefficiency but shows that much of it reflects scale constraints and structurally demanding socio-demographic environments rather than managerial underperformance; several municipalities with high social vulnerability and below-average income attain full efficiency once context is incorporated, while a small set of units emerges as consistent benchmarks. The architecture yields objective evidence for inter-municipal benchmarking, offers a replicable template for data-poor public services, and cautions against context-blind efficiency metrics in allocating social-policy resources.