Machine Learning Based Prediction for Solving Veterinary Data Problems: A Review

Abstract

What if we could detect disease before it manifested itself? While it may appear to be fantasy, it is currently being used in human and animal health by combining advanced computing power with artificial intelligence (AI). Machine learning (ML), a subfield of AI, is a recent approach for developing predictions in many areas of medical science through classification and regression. Using ML to solve veterinary data problems is still unusual. It can aid in farm decision-making processes. ML outperformed statistical models in disease prediction, which produce a high bias in most cases and reduce model reliability when assumptions are violated. Some challenges of ML, such as data size, algorithm tunability, and feature selection, must be considered in order to develop a good predictive model. This review aimed to discuss the role of ML in solving veterinary problems and spotting the light on overcoming the possible challenges, and to encourage the researchers to increase the application of ML over conventional methods.