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Peer-Reviewed Publication
Vet Res Commun2026;50(2):142.February 4, 2026Journal Article

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

Julieta María Decundo1, Alejandro Duitama Leal2,3,4, Julián Andrés Salamanca Bernal2, Susana Nelly Dieguez5,6, Guadalupe Martínez5, Joaquin Mozo5,7, Denisa Soledad Pérez Gaudio5, Carlos Alberto Puentes Morales4, Alejandro Luis Soraci5
1Laboratorio de Toxicología, Departamento de Fisiopatología, Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires, Centro de Investigación Veterinaria de Tandil (CIVETAN, UNCPBA-CICPBA- CONICET), Tandil, Argentina. jdecundo@vet.unicen.edu.ar.
2Fundación Universitaria Los Libertadores, Bogotá, Colombia.
3Fundación Universitaria Politécnico Grancolombano, Bogotá, Colombia.
4Departamento de Matemàticas y Estadìstica, Grupo Signos, Universidad El Bosque, Laboratorio de Soluciones Avanzadas en Virtualización e Inteligencia Artificial (SavIA-Lab), Bogotá, Colombia.
5Laboratorio de Toxicología, Departamento de Fisiopatología, Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires, Centro de Investigación Veterinaria de Tandil (CIVETAN, UNCPBA-CICPBA- CONICET), Tandil, Argentina.
6Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CIC-PBA), Buenos Aires, Argentina.
7Departamento de Producción Animal, Facultad de Ciencias Veterinarias, Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina.
PubMed DOI

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 var…

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