Analyzing the Impact of Smoking and Drinking on Health Metrics in Korea: A Data-driven Predictive Approach

Abstract

This investigation thoroughly explores the effects of smoking and alcohol consumption on health measures within the population of South Korea, using a comprehensive dataset from the National Health Insurance Service of Korea. The study aims to build and validate predictive models by applying logistic regression methodologies, outlier detection, and cross-validation techniques. These models are designed to identify patterns in smoking and drinking behaviors, offering predictive accuracy. The analysis clarifies the relationship between lifestyle choices and key health indicators such as liver enzyme activity, blood fat levels, and heart and blood vessel measurements. The outcomes of this study are ready to significantly contribute to the development of targeted public health strategies, aiming to lessen the risks associated with lifestyle-induced health conditions.