首页|Medical University of Vienna Reports Findings in Machine Learning (Improving Lam eness Detection in Cows: A Machine Learning Algorithm Application)
Medical University of Vienna Reports Findings in Machine Learning (Improving Lam eness Detection in Cows: A Machine Learning Algorithm Application)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting originating in Vienna, Austria, by NewsR x journalists, research stated, “The deployment of diverse datagenerating techn ologies in livestock farming holds the promise of early disease detection and im proved animal well-being. In this paper, we combine routinely collected dairy fa rm and herd data with weather and high frequency sensor data from 6 farms to pre dict new lameness events in various future periods, spanning from the following day to 3 weeks.” The news reporters obtained a quote from the research from the Medical Universit y of Vienna, “A Random Forest classifier, using input features selected by the B oruta Algorithm, was used for the prediction task; effects of individual feature s were further assessed using partial dependence plots. We achieve precision sco res of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the p recision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30% , revealing a substantial a trade-off in model quality between false positives ( false lameness alerts) and false negatives (missed lameness events).”
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