Early Detection and Prediction of Zoonotic Virus Integrated with Machine Learning Techniques

Abstract

Zoonotic virus, which animals transmit to people, remain an increasing threat to human health in the world due to their capacity to propagate rapidly and evolve over time. This paper examines how machine learning can be applied to aid early diagnoses and anticipation of these diseases. It talks about the various forms of infectious entities such as viruses, bacteria, fungi and parasites and the use of animals as carriers. The recent outbreaks have been also reviewed in the paper and the role of learning about the spread of these diseases and the factors that increase the risks have been highlighted. The prediction framework is provided which utilizes the real-life data obtained both in medical and environmental sphere. Data cleaning and preparation are done to be divided into training and testing data. A number of machine learning models, including Decision Tree, KNN, SVM, Logistic Regression, Random Forest, and XGBoost are used and compared. Their output is measured in standard measures in order to estimate which model is the best one in predicting potential infections. The results indicate that the combination of models and the more sophisticated techniques may contribute to the high quality of the predictions and the possibility to take the timely preventive measures. Overall, this study supports the use of data-driven methods to strengthen disease monitoring systems and reduce the impact of zoonotic outbreaks.