A Review of Machine Learning Applications in Veterinary Field

Abstract

Machine learning is a sub field of artificial intelligence which allows forecasting through learning past behaviors and rules from old data. In today's world, machine learning is being used almost in any fields such as education, medicine, veterinary, banking, telecommunication, security, and bio-medical sciences. In human health, although machine learning is generally preferred particularly in predicting diseases and identifying respective risk factors, it is obvious that there are a limited number of publications where this method was applied on veterinary or indicates whether it is correct and applicable. In this review, it was observed that the neural network, logistic regression, linear regression, multiple regression, principle component analysis and k-means methods were frequently used in examined publications and machine learning application in veterinary field upward momentum. Additionally, it was observed that recent developments in the field of machine learning (deep learning, ensemble learning, voice recognition, emotion recognition, etc.) is still new in the field of veterinary. In this review, publications are examined under clustering, classification, regression, multivariate data analysis and image processing topics. This review aims at providing basic information on machine learning and to increase the number of multidisciplinary publications on computer sciences/engineering and veterinary field.