AI-Driven Predictive Modelling for Early Disease Detection and Prevention

Abstract

This paper explores the application of artificial intelligence (AI) in predictive modeling for early disease detection and prevention. We review fundamental concepts of AI-driven predictive modeling, including machine learning algorithms, deep learning techniques, and data mining methods. The study examines various data sources, preprocessing techniques, and modeling approaches used in disease prediction. We discuss feature selection methods, model evaluation techniques, and challenges in implementing AI-driven healthcare solutions. The paper also highlights applications in specific disease domains and emerging trends in the field. Our findings suggest that AI-driven predictive modeling holds significant promise for improving early disease detection and prevention, potentially revolutionizing healthcare practices and outcomes.