ArticleThe Journal of veterinary medical science2026
Machine-learning algorithms to identify key practices contributing to variation in biosecurity levels on Japanese commercial swine farms.
Article in The Journal of veterinary medical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Transboundary diseases, such as African swine fever, pose significant threats to the Japanese swine industry, making robust biosecurity essential. However, established assessment tools quantify overall biosecurity levels, and the specific practices driving performance variation among farms remain unclear. This study applied machine learning algorithms to identify key biosecurity measures distinguishing high-performing farms. Total, external, internal, and diagnostic monitoring scores from 254 records of the biosecurity assessment tool (2014-2023) were analyzed using Random Forest models coupled with Boruta feature selection, incorporating the assessment year to adjust for temporal shifts. The models demonstrated robust predictive accuracy (R
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