Advanced Machine Learning Approach for Disease Outbreak Prediction

Abstract

The rapid spread of infectious diseases across regions highlights the need for early and accurate outbreak prediction systems. This study presents an AI-driven predictive analytics platform designed to forecast global disease outbreaks using data collected from multiple sources. The system gathers information from social media news, historical records, and climate-related data to build a comprehensive dataset. Since real-time data access through APIs is often limited, the required data is collected, stored, and maintained locally for analysis. Textual data from social media is processed using natural language techniques to remove noise and extract meaningful information. Additional preprocessing steps such as handling missing values, normalization, and feature scaling are applied to improve data quality. The processed dataset is then used to train machine learning models including Random Forest, Decision Tree, and Neural Networks. Each model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the Neural Network model achieves the highest prediction accuracy. Visualization techniques are used to analyse disease distribution and affected regions. A Flask-based web application is developed to provide a user-friendly interface for real-time prediction. The system allows users to input news text along with climate conditions to predict possible disease outbreaks. The results demonstrate the effectiveness of combining text analytics and climate data for disease forecasting. Overall, the proposed platform offers a practical solution for early detection and prevention of infectious diseases. Keywords— Artificial Intelligence, Disease Outbreak Prediction, Predictive Analytics, Machine Learning, Neural Networks, Random Forest, Decision Tree, Natural Language Processing, Social Media Data.