Machine Learning Based Approaches for Livestock Symptoms and Diseases Prediction and Classification

Farm animals farmed for financial gain are referred to as livestock. The majority of livestock animals are found in isolated locations with little access to medical care. Rapid and precise disease diagnosis is generally a challenging task. To prevent the disease from spreading among the cattle, it is essential to identify the disease's consequence and take appropriate precautions. Therefore, a system that can assist in forecasting cattle diseases based on symptoms and recommend preventive actions in relation to the projected disease is required. In this work, we analyze a dataset of livestock animal health issues using automated monitoring systems using eight machine learning methods. The variables that the machine learning algorithms examine are the age, symptoms, variations in temperature, disease type. Using a dataset, we have implemented machine learning algorithms for the purpose of early diagnosis and prediction of disease in livestock animals. Machine learning techniques like logistic regression, XGBoost, Catboost, light gradient boost, Random Forests and Support Vector Machines were integrated with feature selection algorithms to predict diseases. After analyzing the data for each classifier, the Catboost and Random Forest classifiers had the greatest and most reliable accuracy of 83.7% and 83.5% respectively.