Application of an integrated Bayesian Network-Artificial Neural Network model in prediction of brand preference

Abstract

The decision-making processes surrounding infant formula selection present significant challenges for public health interventions and market strategists, necessitating robust and interpretable predictive models. This study applied and comparatively evaluated standalone Bayesian Network (BN) and Artificial Neural Network (ANN) models, alongside an integrated BN-ANN architecture, to classify infant formula choices within a Djibouti context. Employing a sequential integration strategy, where BN-derived probabilistic inferences informed the ANN’s feature set, the research analyzed model performance, feature importance, and interpretability. While the standalone BN model offered valuable probabilistic insights into conditional dependencies (Accuracy: ∼0.8500), the standalone ANN demonstrated significantly superior predictive power (Accuracy: 0.9495). Crucially, the integrated BNANN model achieved the highest predictive accuracy (0.9524) and Kappa score (0.9276), indicating a consistent, albeit marginal gain. Feature importance analysis revealed the taste, brand innovation, and completeness in range of baby formula products as the most dominant predictors. Unexpectedly, BN- probabilities contributed minimally as direct ANN features, a phenomenon potentially attributed to information loss from the necessary discretization of continuous variables for the BN, or insufficient variability in the generated probabilities. The study also noted near-perfect classification for one specific formula class, primarily due to highly separable intrinsic features. This research underscores the significant potential of hybrid artificial intelligence for complex multi-class classification, furnishing actionable insights for stakeholders in the infant nutrition sector in Djibouti. Furthermore, it contributes methodologically to the advancements in integrating probabilistic models with deep learning.

Keywords: Bayesian Network; Artificial Neural Network; Hybrid Models; Infant Formula Choice; Predictive Modeling; Feature Importance; Consumer Behavior.