Predicting and Analysing Lumpy Skin Disease Using Ensemble of Machine Learning Models

The economic impact of the infectious bovine virus known as Lumpy Skin Disease (LSD) is substantial. Because of the poxvirus that causes LSD, cattle suffer from decreased meat and milk output, higher veterinary bills, and trade restrictions on cattle-related products. The biggest difficulty in managing LSD epidemics is identifying them at an early stage. Because they are laborious and time-consuming, traditional procedures result in delayed responses. Our research paper provides a comprehensive evaluation of ensemble machine learning models for LSD prediction to help overcome these obstacles. Machine learning models have been shown to be valuable in the field of epidemiology due to their ability to successfully analyse complex data. In this research, a number of ensemble models that have been pre-trained on parameters including location, climate, and disease outbreak history. We also use data preparation methods to equalize data and standardize inputs, for a more reliable study. In order to better understand the patterns of LSD spread, data visualization tools like heatmaps and box plots are used. Our studies' overarching goal is to improve disease management measures by making use of ensemble machine learning models and data analysis strategies, which will have positive effects on cattle health and the economic security of the livestock industry.