Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach.

ABSTRACT: Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the predic

SUBMITTER: Lasser J 

PROVIDER: S-EPMC8601131 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature