A Predictive Model for Headache-Related Depression in Middle- Aged and Older Women Incorporating Individual Treatment Effects

Depression commonly co-occurs with headache, especially among middle-aged and older women, increasing symptom burden and healthcare utilization. To enable early identification and targeted intervention, we developed a machine learning–based risk prediction model and deployed an interactive Shiny web application. Data from 1,930 individuals aged ≥ 45 years with self-reported headache in the 2015 wave of the China Health and Retirement Longitudinal Study were analyzed and randomly split into training and testing subsets. Depressive symptoms were assessed using a validated instrument, and seven predictors spanning clinical and lifestyle domains were selected. Ten machine learning algorithms were compared using discrimination, calibration, and decision curve analysis, with temporal validation on the 2011 CHARLS wave. The gradient boosting machine model achieved the best performance (area under the curve 0.823 training, 0.724 test, 0.785 temporal validation) and favorable calibration. Key predictors included hope, sleep duration, life satisfaction, and self-rated health. Individualized treatment effect analysis identified approximately 5% of participants most likely to benefit from interventions enhancing life satisfaction, and the Qini curve confirmed heterogeneity in treatment effects (uplift area under the curve 55.8). Targeting interventions based on predicted risk can achieve greater benefits than random allocation, supporting precision mental health strategies. This model and its Shiny tool facilitate early identification and tailored intervention for high-risk middle-aged and older women with headache, warranting prospective validation.