Analyzing the impact of heavy metal exposure on osteoarthritis and rheumatoid arthritis: an approach based on interpretable machine learning - Fan, Wenxuan; Pi, Zhipeng; Kong, Keyu; Qiao, Hua; Jin, Minghao; Chang, Yongyun; Zhang, Jingwei; Li, Huiwu (Frontiers in Nutrition, 2024)
This investigation leverages advanced machine learning (ML) techniques to dissect the complex relationship between heavy metal exposure and its impacts on osteoarthritis (OA) and rheumatoid arthritis (RA) utilizing a comprehensive dataset from the National Health and Nutrition Examination Survey (NHANES) spanning from 2003 to 2020. Employing a phased ML strategy that encompasses a range of methodologies, including LASSO regression and SHapley Additive exPlanations (SHAP), this study aims to elucidate the roles specific heavy metals play in the incidence and differentiation of OA and RA. Our analytical framework integrates demographic, laboratory, and questionnaire data through thirteen distinct ML models, applied across seven methodologies to enhance the predictability and interpretability of clinical outcomes. Each phase of the model development was meticulously designed to refine the algorithm's performance progressively, thereby enabling precise identification of key predictors and their contributions to disease outcomes. The results reveal significant associations between certain heavy metals and an increased risk of arthritis, offering new insights into potential pathways for early detection, prevention, and management strategies for arthritis associated with environmental exposures. By improving the interpretability of ML models, this research offers a potent tool for clinicians and researchers, facilitating a deeper understanding of the environmental determinants of arthritis and underscoring the utility of phased machine learning approaches in modern medical research.