AI-Driven Predictive Analytics Framework for Early Detection and Forecasting of Infectious Disease Outbreaks Using Hybrid Machine Learning Models

Abstract

Due to globalization, climate variability, urbanization, and population mobility, infectious disease outbreaks have become frequent and more severe than ever in recent years. Traditional disease surveillance mechanisms are based on slow manual reporting and one-sided epidemiological signals, which would be reactive and do not detect outbreaks early. The given paper introduces an AI-guided predictive analytics platform aimed at detecting and predicting infectious diseases outbreaks earlier by merging diverse data sources and using advanced machine learning solutions. The methodology of the proposed system is to utilize a combination of historical epidemological data, climate variables, data regarding population movement, and text-based information provided by social media to capture complicated spatio-temporal disease patterns. Complete preprocessing pipeline is used, encompassing data cleaning, normalization, balancing of classes and analysis of unstructured text uses Natural Language Processing (NLP). Several machine learning and deep learning models are utilized, such as Random Forest, Decision Tree, and Neural Network architectures, all trained and tested to estimate a risk rating of the outbreak and types of the disease. It has been proved experimentally that the Neural Network model is the best predictor, with the highest accuracy, precision, recall, and F1-score compared with traditional classifiers. By deploying the trained models under a Flaskpowered web application, real-time outbreak prediction, confidence scores, and interactive visualization dashboards are made usable in decision making by the application. The suggested framework allows proactive, population health intervention, effective resource distribution, and better epidemic preparedness. The work continues to show the predictive efficacy of AI-based predictive analytics in enhancing modern disease surveillance mechanisms and in informing the decisions of a data-driven public health.