Predicting Disease Outbreaks using Machine Learning Models on Public Health Data

Timely prediction of disease outbreaks is crucial for effective public health responses. This study explores the use of machine learning models to forecast outbreaks by analyzing public health data, including epidemiological, demographic, and environmental factors. The goal is to create a real-time prediction framework to aid health authorities in early interventions. Models such as time-series forecasting, Random Forest, and Long Short-Term Memory (LSTM) networks are applied to predict diseases like influenza, dengue, and COVID-19. These models are evaluated using accuracy, precision and recall. External factors like weather, population mobility, and public sentiment are also examined for their role in disease spread. The results highlight key factors driving outbreaks and demonstrate how machine learning can enhance public health surveillance by providing early warning systems. This research contributes to the development of scalable, data-driven tools for outbreak prediction, supporting proactive public health strategies and minimizing future impacts.