Exploratory identification of intestinal health and productive performance patterns in post-weaning piglets using explainable machine learning

Abstract

Weaning is a critical stage in swine production, characterized by intestinal alterations that affect piglet health and performance. In this study, machine learning techniques were applied to identify joint patterns between gut health and productivity during the first 15 days post-weaning. A total of 103 animals were analyzed using a dataset of 24 histomorphological, biochemical, and productive variables. Among the unsupervised clustering models, K-means (k = 2) achieved the best separation, revealing two groups with significant differences in intestinal parameters (villus height-to-crypt depth ratio, intestinal absorptive area, duodenal maltase activity, butyric, propionic and total volatile fatty acid concentrations) and performance outcomes (body weight at 15 days and average daily gain). Supervised models were subsequently applied as interpretative tools to assess variable relevance, with Random Forest achieving high internal consistency. SHAP analysis indicated that intestinal morphology, enzymatic activity, and microbial metabolites (particularly total volatile fatty acids, propionate, and butyrate) were most strongly associated with cluster classification. These findings highlight coordinated patterns between intestinal function and growth during the early post-weaning period and suggest that such biomarkers may represent potential targets to be explored in future nutritional strategies. Overall, this study demonstrates the potential of integrating unsupervised explainable machine learning approaches into animal science research for exploratory analysis and hypothesis generation.