Study of the Impact of Smoking and Alcohol Consumption on the Physical Health of South Korean Residents and Development of Machine Learning Predictive Models

Smoking and drinking alcohol consumption have far-reaching impacts on public health, leading to various diseases and influencing individuals' lifestyles. This research delves into the complex relationships between smoking, drinking alcohol consumption, related diseases, and lifestyle factors. Additionally, it explores the development of hybrid machine learning algorithms for predicting these behaviors. The study aims to understand the various factors influencing smoking and drinking patterns and their effects on physical health. These factors are crucial for devising effective intervention strategies and public health campaigns. To quantify these intricate cause-and-effect relationships, this research establishes quantitative measurement methods. These methods contribute to a comprehensive understanding of how smoking and drinking interact with diseases and lifestyle, thus facilitating evidence-based healthcare and policy decisions. This study places a strong emphasis on identifying the most suitable machine learning algorithms for predicting smoking and drinking behaviors. The research goal is to optimize algorithm parameters to improve prediction accuracy and adapt to data sets with different characteristics. The outcomes of this research will contribute to the development of healthier public health strategies, reducing the adverse effects of smoking and drinking on physical health, and developing predictive tools to assist healthcare professionals, policymakers, and individuals in making informed choices and adopting healthier lifestyles.