The association between social risk profile and self-reported severe headache or migraine with all-cause mortality risk: A machine learning-based prediction model and interpretability analysis
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
Objective Social risk factors are key determinants of migraine occurrence and progression. This study assessed the association between the social risk profile (SRP) and the prevalence of self-reported severe headache or migraine and all-cause mortality in US adults, and developed machine learning prediction models to explore feature contributions to internal risk stratification. Methods Using data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004, weighted multivariate logistic regression evaluated the SRP–migraine association, and a weighted Cox proportional hazards model assessed the influence of SRP on all-cause mortality among migraine patients. The Boruta and Lasso algorithms selected predictive features for nine machine learning classifiers to predict migraine risk and four survival models to assess mortality risk. SMOTE was applied within cross-validation folds to address class imbalance. SHAP values were utilized to identify the most critical features. Results Among 11,861 participants, 2,355 self-reported severe headache or migraine; 2,351 were included in the mortality analysis after excluding 4 individuals with missing survival status. Over a median follow-up of 206 months (IQR: 187–224), 471 deaths occurred. Higher SRP scores were associated with lower migraine prevalence (OR = 0.44, 95% CI: 0.34–0.57) and lower all-cause mortality (HR = 0.31, 95% CI: 0.19–0.50). XGBoost achieved the best performance for migraine prediction (AUC = 0.732,
Abstract
Objective Social risk factors are key determinants of migraine occurrence and progression. This study assessed the association between the social risk profile (SRP) and the prevalence of self-reported severe headache or migraine and all-cause mortality in US adults, and developed machine learning prediction models to explore feature contributions to internal risk stratification. Methods Using data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004, weighted multivariate logistic regression evaluated the SRP–migraine association, and a weighted Cox proportional hazards model assessed the influence of SRP on all-cause mortality among migraine patients. The Boruta and Lasso algorithms selected predictive features for nine machine learning classifiers to predict migraine risk and four survival models to assess mortality risk. SMOTE was applied within cross-validation folds to address class imbalance. SHAP values were utilized to identify the most critical features. Results Among 11,861 participants, 2,355 self-reported severe headache or migraine; 2,351 were included in the mortality analysis after excluding 4 individuals with missing survival status. Over a median follow-up of 206 months (IQR: 187–224), 471 deaths occurred. Higher SRP scores were associated with lower migraine prevalence (OR = 0.44, 95% CI: 0.34–0.57) and lower all-cause mortality (HR = 0.31, 95% CI: 0.19–0.50). XGBoost achieved the best performance for migraine prediction (AUC = 0.732, 95% CI: 0.712–0.753), while Random Survival Forest performed best for mortality prediction (AUC = 0.882). SHAP analysis identified age, SRP, and cotinine as key predictors. Decision curve and calibration analyses demonstrated acceptable internal performance, supported by ten-fold cross-validation. Conclusion SRP is an independent predictor of migraine risk and long-term survival. The machine learning analysis provides exploratory insights into feature importance for risk stratification within the development sample, while the regression-based association estimates support the epidemiological significance of social determinants in migraine.