Efficient Classification of Animal Health Conditions Using Gradient Boosting Machine-Based Models

Classifying animal health problems correctly is important for finding problems early and taking action, which leads to better management and animal care. This research looks into how advanced machine learning methods, especially Gradient Boosting Machine (GBM)-based models, can be used to make it easier to tell what kind of health state an animal is in. Three well-known GBM algorithms—XGBoost, LightGBM, and CatBoost—were used. These algorithms are known for being fast and reliable when working with big datasets with lots of dimensions. We chose these models because they can handle both numerical and category data well and because hyperparameters can be tuned in a variety of ways to get the best results. Along with improving the performance of each model separately, we created a blend model that combines the best features of XGBoost, LightGBM, and CatBoost to make classification even more accurate. The mixed method used ensemble learning to cut down on wrong predictions and improve the ability to generalize. These models were used on a large sample that included a lot of different health markers, such as bodily, psychological, and behavioral factors. The combined model did better than the individual GBM models, with higher accuracy, precision, and recall measures for classification. The results of this study show that GBM-based models, especially mixed methods, can be very useful for improving the classification of animal health conditions. More research may be done in the future to see if adding more types of data and real-time tracking can improve model performance even more.