Providing probability distributions for the causal pathogen of clinical mastitis using naive Bayesian networks

Clinical mastitis (CM) can be caused by a wide variety of pathogens and farmers must start treatment before the actual causal pathogen is known. By providing a probability distribution for the causal pathogen, naive Bayesian networks (NBN) can serve as a management tool for farmers to decide which treatment to use. The advantage of providing a probability distribution for the causal pathogen, rather than only providing the most likely causal pathogen, is that the uncertainty involved is visible and a more informed treatment decision can be made. The objective of this study was to illustrate provision of probability distributions for the gram status and for the causal pathogen for CM cases. For constructing the NBN, data were used from 274 Dutch dairy herds in which the occurrence of CM was recorded over an 18-mo period. The data set contained information on 3,833 CM cases. Two-thirds of the data set was used for the construction process and one-third was retained for validation. One NBN was constructed with the CM cases classified according to their gram status, and another was built with the CM cases classified into streptococci, Staphylococcus aureus, or Escherichia coli. Information usually available at a dairy farm was included in both NBN (parity, month in lactation, season of the year, quarter position, SCC and CM history, being sick or not, and color and texture of the milk). Accuracy was calculated to obtain insight in the quality of the constructed NBN. The accuracy of classifying CM cases into gram-positive or gram-negative pathogens was 73%, while the accuracy of classifying CM cases into streptococci, Staph. aureus, or E. coli was 52%. Because only CM cases with a high probability for a single causal pathogen will be considered for pathogen-specific treatment, accuracies based on only classifying CM cases above a particular probability threshold were determined. For instance, for CM cases in which either gram-negative or gram-positive had a probability >0.90, classification according to the gram status reached an accuracy of 97%. We found that the greater the probability for a particular pathogen was for a CM case, the more accurate was the classification of this case as being caused by this pathogen. The probability distributions provided by the NBN and the associated accuracies for varying classification thresholds provide the farmer with considerable insight about the most likely causal pathogen for a CM case and the uncertainty involved.