Machine learning classification of prevalent chronic disease using multidimensional social, behavioral, and psychological determinants: A cross-sectional analysis of the 2024 national health interview survey

作者：Zhang C, Hao J, Lin S · 发表于：Annals of epidemiology · 年份：2026 · DOI：10.1016/j.annepidem.2026.110249

BACKGROUND: Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional clinical risk factors have been extensively studied, the predictive utility of multidimensional social determinants of health (SDOH), including psychosocial factors such as loneliness, psychological distress, and social support, remains inadequately characterized within machine learning (ML) prediction frameworks. OBJECTIVE: To develop and compare ML models that classify the presence of six major chronic conditions-hypertension, type 2 diabetes, coronary heart disease, COPD, depression, and anxiety-using an integrated framework encompassing demographic, socioeconomic, behavioral, psychosocial, healthcare access, functional status, and COVID-19-related predictors; and to quantify the relative predictive importance of each domain across disease categories. METHODS: We conducted a cross-sectional analysis of 32,614 adults from the 2024 NHIS. Twenty-four predictor variables spanning seven SDOH domains were used to train three ML algorithms: Logistic Regression (LR), Gradient Boosting Machine (GBM), and Random Forest (RF). Model performance was evaluated using 5-fold stratified cross-validation with AUROC, F1 score, and AUPRC. Subgroup analyses were performed by age, sex, and race/ethnicity. Because the design is cross-sectional, the models estimate the likelihood of prevalent disease, not incident risk; robustness was confirmed wi...