Predictive Modelling to Understand and Combat Smoking Using Machine Learning: A Review

Abstract

Abstract: Smoking remains a worldwide public health issue, with significant personal consequences well-being and society as a whole. Traditional approaches to understanding and combatting smoking have their limitations, and in recent years, machine learning has become a viable instrument to tackle this problem. This review article provides a comprehensive overview of predictive modeling to understand and combat smoking using machine learning. We delve into the diverse data sources and preprocessing techniques, feature engineering approaches, and machine learning models employed in the context of smoking prediction. The review categorizes studies into smoking initiation and smoking cessation prediction, shedding light on the methodologies, results, and challenges in each domain. Furthermore, we explore the real-world applications of predictive modeling in smoking control, emphasizing their impact on public health policy and awareness campaigns. Ethical considerations and challenges related to bias, privacy, and model interpretability are also discussed. The paper concludes by suggesting future research directions and emphasizing the crucial role of machine learning in comprehensively addressing the smoking epidemic..