A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

peer-reviewedThe aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling

A comparison of machine learning techniques for predicting insemination outcome in Irish dairy cows

Abstract. Reproductive performance has an important effect on economic efficiency in dairy farms with short yearly periods of breeding. The individual factors affecting the outcome of an artificial insemination have been extensively researched in many univariate models. In this study, these factors are analysed in combination to create a comprehensive multivariate model of conception in Irish dairy cows. Logistic regression, Naïve Bayes, Decision Tree learning and Random Forests are trained using 2,723 artificial insemination records from Irish research farms. An additional 4,205 breeding events from commercial dairy farms are used to evaluate and compare the performance of each data mining technique. The models are assessed in terms of both discrimination and calibration ability. The logistic regression model was found to be the most useful model for predicting insemination outcome. This model is proposed as being appropriate for use in decision support and in general simulation of Irish dairy cows

A comparison of machine learning techniques for predicting insemination outcome in Irish dairy cows

peer-reviewedReproductive performance has an important effect on economic efficiency in dairy farms with short yearly periods of breeding. The individual factors affecting the outcome of an artificial insemination have been extensively researched in many univariate models. In this study, these factors are analysed in combination to create a comprehensive multivariate model of conception in Irish dairy cows. Logistic regression, Naive Bayes, Decision Tree learning and Random Forests are trained using 2,723 artificial insemination records from Irish research farms. An additional 4,205 breeding events from commercial dairy farms are used to evaluate and compare the performance of each data mining technique. The models are assessed in terms of both discrimination and calibration ability. The logistic regression model was found to be the most useful model for predicting insemination outcome. This model is proposed as being appropriate for use in decision support and in general simulation of Irish dairy cows

Evaluating Information Filtering Techniques in an Adaptive Recommender System

Abstract. With the huge increase in the volume of information available in digital form and the increasing diversity of Web applications, the need for efficient, reliable, information filtering is critical. New algorithms that filter information for a specific taste are being developed to tackle the problem of information overload. This paper proposes that there is a substantial relative difference in the performances of various filtering algorithms as they are applied to different datasets, and that these performance differences can be leveraged to form the basis of an Adaptive Information Filtering System. We classify five different datasets based on a number of metrics, including sparsity, ratings distribution and useritem ratio, and develop a regression function over these metrics to predict the suitability of a particular recommendation algorithm for a previously unseen dataset. Our results show that the predicted best algorithm does perform best on the new dataset.