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Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks

Ehret, A. ; Hochstuhl, D. ; Krattenmacher, N. ; Tetens, J. ; Klein, M. S. ; Gronwald, Wolfram ; Thaller, G.



Abstract

Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods ...

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