Animal Disease Prediction Using ML

The rising concern over animal health and welfare necessitates the development of efficient and accurate disease prediction methods. The importance of this search lies in its capability to bridge the differences in disease prediction. By studying data from symptoms, and using technologies such as frontend tool, this framework aims to provide a cost-effective, easily accessible solution, and sustainable solution for all stakeholders in animal health ecosystem. The research methodology involves the collection and pre-processing of various datasets including different animals’ species, diseases, and symptoms. These data have been thoroughly analyzed to identify patterns and correlations between various health parameters. The initial model is generated using the machine learning algorithm Random Forest. The experimental results demonstrate the effectiveness of the proposed models in accurately predict animal diseases based on input parameters using traditional diagnostic methods.