Paper Title
LEVERAGING ARTIFICIAL INTELLIGENCE IN EPIDEMIOLOGY: PREDICTIVE ANALYTICS FOR DISEASE MONITORING AND OUTBREAK PREVENTION

Abstract
Artificial intelligence (AI) adoption has become crucial for epidemiology to handle quick-changing infectious diseases under modern globalization. The advancement of disease observation tools and outbreak predictive systems utilizes artificial intelligence because traditional procedures cannot match up to present day health crisis patterns and their rapid development. The proposed robust AI-based predictive disease analysis system uses MIMIC-III dataset to detect diseases early through analysis of more than 40,000 intensive care unit (ICU) patient clinical data. The MIMIC-III dataset became ready for modeling after performing several preprocessing phases which combined feature encoding along with feature selection methods together with SMOTE-based balance implementation. The results validated the Attention Net as the most accurate predictor of diseases and outbreaks reaching 84.62% accuracy. This classifier, with its excellent performance, highlights the potential for real-time disease surveillance systems. AI applications in epidemiology will gain future predictive capabilities through combination of various datasets and multilayer neural networks to achieve more effective disease prediction and outbreak prevention. Keywords - Artificial Intelligence (AI), Disease Prediction, Epidemiology, Intensive Care Unit (ICU), Predictive Analysis.