Published Paper Details:

Animal disease prediction is a critical component of effective animal health management and plays a vital role in preventing the spread of infectious dis eases, ensuring the well-being of livestock, and safeguarding public health. Lever aging the power of machine learning (ML) techniques, this study aims to develop a predictive model for the early detection and forecasting of animal dis eases, enabling proactive intervention and timely preventive measures. The pro- posed ML-based framework integrates diverse data sources, including environ- mental factors, animal behavior patterns, genetic information, and historical disease records, to build robust predictive models capable of identifying potential disease outbreaks and assessing the susceptibility of animal populations to specific diseases. Through comprehensive data preprocessing, feature selection, and model development, the system seeks to provide accurate and reliable pre- dictions, facilitating informed decision-making for veterinarians, animal health professionals, and policymakers. By harnessing the capabilities of real-time monitoring and continuous model updates, the proposed system aims to offer a dynamic and proactive approach to animal disease management, contributing to the overall improvement of animal health, welfare, and sustainable agricultural practices. The findings from this research have the potential to significantly enhance the efficiency of disease control measures, minimize economic losses in the agricultural sector, and promote a healthier and more resilient animal population.