A Systematic Review of Machine Learning Applications in Infectious Disease Prediction, Diagnosis, and Outbreak Forecasting

Source abstract

Infectious diseases pose a significant global health burden, contributing to millions of deaths annually despite advancements in sanitation and healthcare access. This review systematically examines the role of machine learning in infectious disease prediction, diagnosis, and outbreak forecasting in the United States. We first categorize existing studies according to the type of disease and the ML methodology, highlighting key findings and emerging trends. We then examine the integration of hybrid and deep learning models, the application of natural language processing (NLP) in public health monitoring, and the use of generative models for medical image enhancement. In addition, we discuss the applications of machine learning in five diseases, including coronavirus disease 2019 (COVID-19), influenza (flu), human immunodeficiency virus (HIV), tuberculosis, and hepatitis, focusing on its role in diagnosis, outbreak prediction, and early detection. Our findings suggest that while machine learning has significantly improved disease detection and prediction, challenges remain in model generalizability, data quality, and interpretability.