Integrating diverse data sources to predict disease risk in dairy cattle—a machine learning approach.
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| Title: | Integrating diverse data sources to predict disease risk in dairy cattle—a machine learning approach. |
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| Authors: | Lasser, Jana1,2,3 (AUTHOR), Matzhold, Caspar1,3 (AUTHOR), Egger-Danner, Christa4 (AUTHOR), Fuerst-Waltl, Birgit5 (AUTHOR), Steininger, Franz4 (AUTHOR), Wittek, Thomas6 (AUTHOR), Klimek, Peter1,3 (AUTHOR) peter.klimek@meduniwien.ac.at |
| Source: | Journal of Animal Science. Nov2021, Vol. 99 Issue 11, p1-14. 14p. |
| Document Type: | Article |
| Subjects: | Dairy cattle, Machine learning, Sensitivity & specificity (Statistics), Animal herds, Sports nutrition, Livestock farms, Mastitis, Animal breeding |
| Geographic Terms: | Austria |
| 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. [ABSTRACT FROM AUTHOR] |
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| Author Affiliations: | 1Section for Science of Complex Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna , 1090 Vienna , Austria 2Institute for Interactive Systems and Data Science, Graz University of Technology , 8010 Graz , Austria 3Complexity Science Hub Vienna , 1080 Vienna , Austria 4ZuchtData EDV-Dienstleistungen GmbH , 1200 Vienna , Austria 5Division of Livestock Sciences, University of Natural Resources and Life Sciences , 1180 Vienna , Austria 6Vetmeduni Vienna, University Clinic for Ruminants , 1210 Vienna , Austria |
| ISSN: | 0021-8812 |
| DOI: | 10.1093/jas/skab294 |
| Accession Number: | 153797259 |
| Database: | Veterinary Source |
| 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00218812 |
| DOI: | 10.1093/jas/skab294 |