Using machine learning algorithms to predict the occurrence of clinical mastitis in Holstein cows

Mastitis is one of the most frequent and costly diseases of the dairy cattle industry and causes many economic losses, which negatively affects milk yield and composition, fertility, longevity and welfare of cows. The best solution for reducing the economic and biological consequences is early and accurate prediction of mastitis based on indicator factors. So far, various statistical methods have been used to predict mastitis such as linear and multiple regression, and threshold models. Machine learning is another method that has recently widely been used to predict farm profitability, reproductive traits, longevity and abortion in dairy cow. Machine learning is defined as a set of methods for automatically finding patterns in data and then using those patterns to predict possible future data.

In this research, the performance of four machine learning algorithms including random forest, decision tree, Naïve Bayes and logistic regression and two sampling methods, over-sampling and under-sampling, were compared to predict risk of clinical mastitis based on data collected in two Holstein dairy herds in Isfahan province. Final dataset included 393504 records on cows calved during 2007 to 2017 of which 13653 cases (3.47%) were infected and 379851 cases (96.53%) were healthy. Factors related to mastitis, including parity, daily milk production, calving