Abstract
AIMS: The epidemiology and age-specific patterns of lifetime suicide attempts (LSA) in China remain unclear. We aimed to examine age-specific prevalence and predictors of LSA among Chinese adults using machine learning (ML). METHODS: We analyzed 25,047 adults in the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR-2024), stratified into three age groups (18-24, 25-44, ≥ 45 years). Thirty-seven candidate predictors across six domains-sociodemographic, physical health, mental health, lifestyle, social environment, and self-injury/suicide history-were assessed. Five ML models-random forest, logistic regression, support vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Naive Bayes-were compared. SHapley Additive exPlanations (SHAP) were used to quantify feature importance. RESULTS: The overall prevalence of LSA was 4.57% (1,145/25,047), with significant age differences: 8.10% in young adults (18-24), 4.67% in adults aged 25-44, and 2.67% in older adults (≥45). SVM achieved the best test-set performance across all ages [area under the curve (AUC) 0.88-0.94, sensitivity 0.79-0.87, specificity 0.81-0.88], showing superior calibration and net clinical benefit. SHAP analysis identified both shared and age-specific predictors. Suicidal ideation, adverse childhood experiences, and suicide disclosure were consistent top predictors across all ages. Sleep disturbances and anxiety symptoms stood out in young adults; marital status, living alone, and perceived stress in mid-life; and functional limitations, poor sleep, and depressive symptoms in older adults. CONCLUSIONS: LSA prevalence in Chinese adults is relatively high, with a clear age gradient peaking in young adulthood. Risk profiles revealed both shared and age-specific predictors, reflecting distinct life-stage vulnerabilities. These findings support age-tailored suicide prevention strategies in China.