Combining real data and expert knowledge to build a Bayesian Network — Application to assess multiple risk factors for fall among elderly people

• Published in: Expert systems with applications 2024-10, Vol.252, p.124106, Article 124106
• Main Authors: Sihag, Gulshan, Delcroix, Véronique, Grislin-Le Strugeon, Emmanuelle, Siebert, Xavier, Piechowiak, Sylvain, Puisieux, François
• Format: Article
• Language: English
• Subjects: Bayesian Network; Classification; Computer Science; Data science; Expert knowledge; Imbalanced data; Knowledge model; Risk factors for fall
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.124106

Building a prediction model based on both real data and expert knowledge can be challenging. We experienced this difficulty in our context of predicting fall risk factors in the elderly, where two sources of data were available: hand-reported information from medical consultations and expert information from human specialists. As is often the case, the real data were incomplete and imbalanced, and the participation of medical experts required the selection of a kind of model they can understand. This article describes the steps followed in the construction and evaluation of the model, structured as a new framework. The proposed model is a Bayesian network (BN) built from expert knowledge and learning from a real data set, guided by both the understandability of the BN graph and the performance of the model to predict risk factors. The resulting Bayesian network model includes 90 variables to evaluate 10 actionable target risk factors. It demonstrated its potential compared to other classifiers in terms of prediction performance, whilst offering a higher degree of interpretability. •Build a Bayesian Network (BN) by combining expert knowledge and a real data set.•Propose a complete framework to build an efficient and understandable BN model.•Propose a knowledge model including 90 variables and 10 target risk factors for fall.•Predict 10 risk factors for fall and compare with usual classifiers.•Improve the understandability of the BN model thanks to local graphical models.