The Cleaning Simulation: Applying Predictive Decision Trees for Chemical Exposure Risks and Asthma-Like Symptoms in Laboratory Workers

Exposure to chemical irritants in laboratory and medical environments poses significant health risks to workers, particularly in relation to asthma-like symptoms. Routine cleaning practices, which often involve the use of strong chemical agents to maintain hygienic settings, have been shown to contribute to respiratory issues. Laboratories, where chemicals such as hydrochloric acid and ammonia are frequently used, represent an underexplored context in the study of occupational asthma. While much of the research on chemical exposure has focused on industrial and high-risk occupations or large cohort populations, less attention has been given to the less obvious risks in laboratory and medical environments. Given the growing reliance on cleaning agents to maintain sterile and safe workspaces in scientific research and healthcare facilities, I find this gap particularly concerning. In my study, I used a simulated cohort based on key demographic and exposure patterns from foundational research to assess the impact of chemical exposure from cleaning products in laboratory environments. I applied four supervised machine learning models to evaluate the relationship between chemical exposures and asthma-like symptoms: (1) Decision Trees, (2) Random Forest, (3) Gradient Boosting, and (4) XGBoost. I found that high exposures to hydrochloric acid and ammonia were significantly associated with asthma-like symptoms, and workplace type also played a critical role in determining asthma risk. This research provides a data-driven framework for assessing and predicting asthma-like symptoms in workers exposed to cleaning agents and highlights the potential for integrating predictive modeling into occupational health and safety monitoring. Future work should explore dose-response relationships and the temporal dynamics of chemical exposure to further refine these models and better understand long-term health risks.