Machine Learning Analysis of a Harmonized Multi-Experiment Dataset Suggests Phase-Dependent Predictability of Weight Gain in Salmonella-Challenged Pigs

This study aimed to evaluate the internal predictability of weight gain using an integrated dataset derived from four experiments conducted with growing–finishing pigs subjected to a common Salmonella Typhimurium challenge protocol. Individual-animal data from 230 pigs were harmonized into a database containing growth performance, body composition, serum biochemical measurements, and experiment-level information. Relative weight gain, calculated relative to body weight at the beginning of each evaluation period, was evaluated separately during the acute phase, from days 0 to 7 post-challenge (RWG7; n = 132), and the chronic phase, from days 7 to 28 post-challenge (RWG7–28; n = 131). Linear Model, Elastic Net, Random Forest, XGBoost, Generalized Additive Models, and Support Vector Machine were compared using individual-level five-fold cross-validation, in which pigs from the same source experiment could occur in both training and validation folds. Single regression trees were additionally fitted as exploratory tools. During the acute phase, cross-validated R2 values ranged from 0.078 to 0.186, indicating that the available baseline variables explained only a limited proportion of the variation in RWG7. Predictive performance was higher during the chronic phase, particularly for the Linear Model and Elastic Net, which showed essentially equivalent cross-validated performance, with R2 values of 0.419 and 0.420, respectively. Random Forest and XGBoost achieved high in-sample performance but substantially lower out-of-fold performance, suggesting overfitting structures within the integrated dataset. Body weight, body composition, biochemical variables, and the source experiment contributed to model predictions, indicating that the predictive signal incorporated information related to both individual physiological status and between-experiment heterogeneity. Overall, acute-phase growth performance was poorly predicted from baseline information, whereas chronic-phase growth showed a stronger internal predictive signal. This comparison reflects differences in the predictor information available at the beginning of each phase, as chronic-phase models additionally incorporated body weight and relative weight gain measured on day 7. These results should be interpreted as exploratory and hypothesis-generating because experiment-held-out or external validation is required before extrapolation to independent experimental or commercial populations.