Towards Sustainable Equine Welfare: Comparative Analysis of Machine Learning Techniques in Predicting Horse Survival

Abstract

Promoting sustainable equine welfare is pivotal in ensuring the well-being of horses, particularly concerning their survival based on past medical conditions. This study presents a comprehensive comparative analysis of various machine learning techniques employed to predict the survival prospects of horses using historical medical data. By leveraging a dataset encompassing diverse medical attributes and survival outcomes, this research assesses the efficacy and comparative performance of distinct machine learning algorithms. The study delves into the application of supervised learning models, including but not limited to decision trees, random forests, support vector machines, and neural networks, in predicting equine survival. Evaluative metrics such as accuracy, precision, recall, and F1 score are employed to assess the predictive capabilities and generalizability of each model. Moreover, this research emphasizes the importance of sustainable equine welfare within the broader context of responsible animal care.