Making Better Medical Decisions Using Machine Learning: A Bayesian Model
Journal
Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications
ISSN
1860-949X
1860-9503
Publisher
Springer Nature Switzerland
Date Issued
2025
Author(s)
Type
text::book::book part
Abstract
Currently, most countries seek universal health coverage for all people; however, in the face of the crisis in health systems caused by the COVID-19 pandemic and the high demand for these services, it is more relevant to have tools that allow faster data taking. However, making medical decisions is one of the most complex processes. Artificial intelligence (AI) is a rapidly evolving field that can transform various aspects of healthcare, such as diagnosis, treatment, prevention, and management. However, to have confidence in the systems, the actors must ensure they are adequately trained to make correct decisions. This research analyzes medical decision-making through Bayesian networks with machine learning techniques. This research creates a methodology and model for making medical decisions based on artificial intelligence. The model shows critical factors that optimally influence decision-making to generate value that translates into patient health. The results show that optimal or non-optimal medical decision-making and its various aspects through the causality of the variables allow the interrelation to be more adequately captured to manage it. The most relevant factors for adequate decision-making are Ethical Issues, Risk/Benefit, Scientific Integrity, Transparent Decisions, Data Preprocessing and Curation, Performance Evaluation, and ML Model. ©The authors © Springer.
License
Acceso Restringido
How to cite
Terán-Bustamante, A., Martínez-Velasco, A. (2026). Making Better Medical Decisions Using Machine Learning: A Bayesian Model. In: Moya-Albor, E., Ponce, H., Brieva, J., Gomez-Coronel, S.L., Torres, D.R. (eds) Machine Learning Methods in Biomedical Field. Studies in Computational Intelligence, vol 1218. Springer, Cham. https://doi.org/10.1007/978-3-031-96328-5_13