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dc.contributor.authorHenpstalk, K.*
dc.contributor.authorMcParland, Sinead*
dc.contributor.authorBerry, Donagh*
dc.identifier.citationK. Hempstalk, S. McParland, D.P. Berry. Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows. Journal of Dairy Science, 2015, 98(8), 5262-5273. DOI:
dc.description.abstractThe ability to accurately predict the conception outcome for a future mating would be of considerable benefit for producers in deciding what mating plan (i.e., expensive semen or less expensive semen) to implement for a given cow. The objective of the present study was to use herd- and cow-level factors to predict the likelihood of conception success to a given insemination (i.e., conception outcome not including embryo loss); of particular interest in the present study was the usefulness of milk mid-infrared (MIR) spectral data in augmenting the accuracy of the prediction model. A total of 4,341 insemination records with conception outcome information from 2,874 lactations on 1,789 cows from 7 research herds for the years 2009 to 2014 were available. The data set was separated into a calibration data set and a validation data set using either of 2 approaches: (1) the calibration data set contained records from all 7 farms for the years 2009 to 2011, inclusive, and the validation data set included data from the 7 farms for the years 2012 to 2014, inclusive, or (2) the calibration data set contained records from 5 farms for all 6 yr and the validation data set contained information from the other 2 farms for all 6 yr. The prediction models were developed with 8 different machine learning algorithms in the calibration data set using standard 10-times 10-fold cross-validation and also by evaluating in the validation data set. The area under curve (AUC) for the receiver operating curve varied from 0.487 to 0.675 across the different algorithms and scenarios investigated. Logistic regression was generally the best-performing algorithm. The AUC was generally inferior for the external validation data sets compared with the calibration data sets. The inclusion of milk MIR in the prediction model generally did not improve the accuracy of prediction. Despite the fair AUC for predicting conception outcome under the different scenarios investigated, the model provided a reasonable prediction of the likelihood of conception success when the high predicted probability instances were considered; a conception rate of 85% was evident in the top 10% of inseminations ranked on predicted probability of conception success in the validation data set.en_US
dc.publisherElsevier for American Dairy Science Associationen_US
dc.relation.ispartofseriesJournal of Dairy Science;vol 98
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.subjectDairy Cattleen_US
dc.titleMachine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cowsen_US
dc.contributor.sponsorEuropean Commissionen_US

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