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

Jana Lasser, Caspar Matzhold, Christa Egger-Danner, Birgit Fuerst-Waltl, Franz Steininger, Thomas Wittek, Peter Klimek*

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung

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 prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.
Originalspracheenglisch
Aufsatznummerskab294
FachzeitschriftJournal of Animal Science
Jahrgang99
Ausgabenummer11
DOIs
PublikationsstatusVeröffentlicht - 1 Nov. 2021

ASJC Scopus subject areas

  • Lebensmittelwissenschaften
  • Genetik
  • Nutztierwissenschaften und Zoologie

Fields of Expertise

  • Information, Communication & Computing

Fingerprint

Untersuchen Sie die Forschungsthemen von „Integrating diverse data sources to predict disease risk in dairy cattle— a machine learning approach“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren