Early detection of subclinical mastitis in lactating dairy cows using cow-level features.

Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate its effect. The present study investigated how well predictive models built using machine learning techniques can detect subclinical mastitis up to 7 d before its occurrence. The data set used consisted of 1,346,207 milk-day (i.e., a day when milk was collected on both morning and evening) records spanning 9 yr from 2,389 cows producing on 7 Irish research farms. Individual cow composite milk yield and maximum milk flow were available twice daily, whereas milk composition (i.e., fat, lactose, protein) and somatic cell count (SCC) were collected once per week. Other features describing parity, calving dates, predicted transmitting ability for SCC, body weight, and history of subclinical mastitis were also available. The results of the study showed that a gradient boosting machine model trained to predict the onset of subclinical mastitis 7 d before a subclinical case occurs achieved a sensitivity and specificity of 69.45 and 95.64%, respectively. Reduced data collection frequency, where milk composition and SCC were recorded only every 15, 30, 45, and 60 d was simulated by masking data, to reflect the frequency of recording of this data on commercial dairy farms in Ireland. The sensitivity and specificity scores reduced as recording frequency reduced with respective scores of 66.93 and 80.43% when milk composition and SCC were recorded just every 60 d. Results demonstrate that models built on data that could be recorded routinely available on commercial dairy farms, can achieve useful predictive ability of subclinical mastitis even with reduced frequency of milk composition and SCC recording.

The problem of bias in training data in regression problems in medical decision support.

This paper describes a bias problem encountered in a machine learning approach to outcome prediction in anticoagulant drug therapy. The outcome to be predicted is a measure of the clotting time for the patient; this measure is continuous and so the prediction task is a regression problem. Artificial neural networks (ANNs) are a powerful mechanism for learning to predict such outcomes from training data. However, experiments have shown that an ANN is biased towards values more commonly occurring in the training data and is thus, less likely to be correct in predicting extreme values. This issue of bias in training data in regression problems is similar to the associated problem with minority classes in classification. However, this bias issue in classification is well documented and is an on-going area of research. In this paper, we consider stratified sampling and boosting as solutions to this bias problem and evaluate them on this outcome prediction problem and on two other datasets. Both approaches produce some improvements with boosting showing the most promise.