Untangling the complexity of diabetes risk: a Bayesian approach to learning causal structures

• Autor: Lituma Villamar, Ney Michel
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad de Huánuco
• Repositorio: Revistas - Universidad de Huánuco
• Lenguaje: español; inglés
• OAI Identifier: oai:ojs2.localhost:article/871
• Enlace del recurso: http://revistas.udh.edu.pe/RPCS/article/view/871
• Nivel de acceso: acceso abierto
• Materia: diabetes mellitus; Bayesian networks; artificial intelligence; body mass index; hypertension; glycated hemoglobin A; algorithms; risk factors; prognosis; early diagnosis; redes bayesianas; inteligencia artificial; índice de masa corporal; hipertensión; hemoglobina a glucosilada; algoritmos; factores de riesgo; pronóstico; diagnóstico precoz

Objective: To evaluate the performance and interpretability of Bayesian network classifiers for the early detection of diabetes. Methods: A model validation study of machine learning applied to healthcare was conducted, focusing on performance assessment and explainability of algorithms on a categorical and preprocessed dataset. Specifically, the following classifiers were trained and applied: Naive Bayes, Tree Augmented Naive–Chow-Liu (TAN–Chow-Liu), Tree Augmented Naive–Hill Climbing with Super Parents (TAN–HCSP), Fast Super-Parent Search with Joint Mutual Information (FSSJ), and the K-Dependence Bayesian Classifier (KDB). Models were tested on 100,000 preprocessed records (filtered by causal relevance and variable discretization) using bnlearn and bnclassify. Data were partitioned 75/25 (training/testing), and accuracy, sensitivity, specificity, and F1 score were estimated. In addition, the learned structures were analyzed against clinical evidence. Results: All models achieved accuracy >= 0.95 and F1 score > 0.94. FSSJ showed the best performance (accuracy 0.97; specificity 1.00), while Naive Bayes and KDB achieved comparable metrics with lower computational cost. The learned networks reproduced known associations among body mass index (BMI), hypertension, HbA1c, and glucose, and identified indirect chains (e.g., age influencing BMI, BMI influencing glucose, and glucose influencing diabetes), reinforcing their clinical plausibility. Conclusions: Bayesian networks provide transparent, high-quality predictions for diabetes risk. Basic architectures can perform on par with more complex variants when preprocessing is rigorous. The causal pathways highlight modifiable factors (overweight, elevated blood pressure) as priority targets for preventive interventions.