Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique.

ABSTRACT: Surgical castration of males is carried out on a large scale in the US swine industry and the pain resulting from this procedure can be assessed using the Unesp-Botucatu pig composite acute pain scale (UPAPS). We aim to propose a short version of UPAPS based on the behaviors best-ranked by a random forest algorithm. We used behavioral observations from databases of surgically castrated pre-weaned and weaned pigs. We trained a random forest algorithm using the pain-free (pre-castration) and painful (post-castration) conditions as target variable and the 17 UPAPS pain-altered behaviors as feature variables. We ranked the behaviors by their importance in diagnosing pain. The algorithm was refined using a backward step-up procedure, establishing the Short UPAPS. The predictive capacity of the

SUBMITTER: Pivato GM 

PROVIDER: S-EPMC11871345 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature