Seon-woo Kim ^1
1Department of Pathology, College of Korean Medicine, Gachon University, Seongnam, Republic of Korea

ABSTRACT

This study investigates the revolutionary potential of predictive modeling in identifying and limiting carcinogen exposure to improve cancer prevention techniques. Our study explores the complex interactions between environmental, occupational, and lifestyle variables to create predictive models that estimate the risks related to carcinogen exposure. We do this by using the broad field of big data analytics. The geographical dimension is one key factor that allows for the personalization of interventions based on regional differences in environmental features and industrial landscapes. Occupational environments, which are frequently high-risk ones, are examined to find patterns of exposure that may be used to influence specific occupational health and safety regulations. Evaluations of the air and water quality provide important insights that help legislators adopt exact pollution control measures and shape sustainable urban development. Lifestyle variables provide individualized risk evaluations since they are deeply integrated into predictive modeling. This multifaceted investigation provides guidance for population-wide interventions and individual preventative techniques, pushing public health policies toward proactive measures. The appropriate use of big data requires careful consideration of data privacy and ethical issues. Robust ethical frameworks maintain a difficult balance between preserving individual privacy and extracting significant insights, ensuring that scientific discoveries are realized with the highest regard for ethical principles. Predictive modeling is used in many fields, such as research and development, environmental management, public health policy, occupational health and safety, cancer prevention techniques, and healthcare resource allocation. According to this study, big data analytics and predictive modelling played crucial roles in the fight against cancer in the future, ushering in a new era of proactive, evidence-based treatments for a stronger, healthier society.

Keywords:Predictive Modeling (PM), Carcinogen Exposure (CE), Big Data Analytics (BDA), Smart PLS Algorithm.