  The aim of this study was to employ machine learning algorithms based on
sensor behavior data for (1) early-onset detection of digital dermatitis (DD);
and (2) DD prediction in dairy cows. With the ultimate goal to set-up early
warning tools for DD prediction, which would than allow a better monitoring and
management of DD under commercial settings, resulting in a decrease of DD
prevalence and severity, while improving animal welfare. A machine learning
model that is capable of predicting and detecting digital dermatitis in cows
housed under free-stall conditions based on behavior sensor data has been
purposed and tested in this exploratory study. The model for DD detection on
day 0 of the appearance of the clinical signs has reached an accuracy of 79%,
while the model for prediction of DD 2 days prior to the appearance of the
first clinical signs has reached an accuracy of 64%. The proposed machine
learning models could help to develop a real-time automated tool for monitoring
and diagnostic of DD in lactating dairy cows, based on behavior sensor data
under conventional dairy environments. Results showed that alterations in
behavioral patterns at individual levels can be used as inputs in an early
warning system for herd management in order to detect variances in health of
individual cows.

Machine Learning;Signal Processing