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Comparative performance analysis of three machine learning algorithms applied to sensor data in dairy cattle to predict metritis events I. Behaviors measured with an ear-tag accelerometer.

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Comparative performance analysis of three machine learning algorithms applied to sensor data in dairy cattle to predict metritis events I. Behaviors measured with an ear-tag accelerometer.
 
Identifier https://doi.org/10.7910/DVN/BZX8KD
 
Creator Vidal, Gema
Sharpnack, James
Pinedo, Pablo
Tsai, I Ching
Lee, Amanda Renee
 
Publisher Harvard Dataverse
 
Description Dairy cattle behavioral data measured by an ear-tag accelerometer during the first 21 days postpartum was used to build predictive models for metritis events. Three machine learning classifiers were used to compare performance in terms of F1 score, using a rank-based method due to the unbalanced nature of the dataset. Performance for each combination of classifier, sensor data aggregation, and time before the event are reported at different cut-offs based on class probabilities.
 
Subject Medicine, Health and Life Sciences
predictive modeling
classification algorithms
precision dairy technology
postpartum period
dairy cattle behavior
 
Contributor Vidal, Gemma