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Predicting the occurrence of disease outbreaks in aquacultural farms can be of considerable value to the long-term sustainable development of the industry. Prior research on disease prediction has essentially depended upon traditional statistical models with varying degrees of prediction accuracy. Furthermore, the application of these models in sustainable aquaculture development and in controlling environmental deterioration has been very limited. In an attempt to look for a more reliable model, we developed a probabilistic neural network (PNN) to predict shrimp disease outbreaks in Vietnam using farm-level data from 480 Vietnamese shrimp farms, including 86 semi-intensive and 394 extensive farms. We also compared predictive performance of the PNN against the more traditional logistic regression approach on the same data set. Disease occurrence (a 0–1 variable) is hypothesized to be affected by a set …