Disease Prediction in Cattle: A Mixed-Methods Review of Predictive Modeling Studies.

Abstract

Predictive models use historical data to predict a future event and can be applied to a wide variety of tasks. A broader evaluation of the cattle literature is required to better understand predictive model performance across various health challenges and to understand data types utilized to train models. This narrative review aims to describe predictive model performance in greater detail across various disease outcomes, input data types, and algorithms with a specific focus on accuracy, sensitivity, specificity, and positive and negative predictive values. A secondary goal is to address important areas for consideration for future work in the beef cattle sector. In total, 19 articles were included. Broad categories of disease were covered, including respiratory disease, bovine tuberculosis, and others. Various input data types were reported, including demographic data, images, and laboratory test results, among others. Several algorithms were utilized, including neural networks, linear models, and others. Accuracy, sensitivity, and specificity values ranged widely across disease outcome and algorithm categories. Negative predictive values were greater than positive predictive values for most disease outcomes. This review highlights the importance of utilizing several performance metrics and concludes that future work should address prevalence of outcomes and class-imbalanced data.