Predicting perceived likelihood of future suicide attempts in youth with non-suicidal self-injury: A machine learning approach using LightGBM and SHAP.

Journal: Journal of affective disorders
Published Date: Dec 19, 2025

Abstract

OBJECTIVES: Non-suicidal self-injury (NSSI) is a strong predictor and a gateway to suicide attempts (SA) among youth. Therefore, understanding how individuals with NSSI develop thoughts of SA is crucial for early prevention. This study aims to analyze which factors can predict the perceived likelihood of future suicide attempts (PLFSA) among youth engaged in NSSI. METHODS: Among the 96,218 respondents who completed the survey, 6955 participants (with NSSI history but without lifetime SA) were stratified into three groups based on their NSSI frequency over the past year: 0 days (N = 2951), 1-4 days (N = 2350), and 5 days or more (N = 1654). We employed the Light Gradient-Boosting Machine (LightGBM) algorithm with 5-fold cross-validation to build predictive models based on 56 features across 12 categories. To enhance the interpretability of the models, Shapley Additive Explanations (SHAP) were applied to assess feature importance. RESULTS: Machine learning models effectively predicted the PLFSA in individuals with NSSI (AUC = 0.85-0.95), confirming known influencing factors (i.e., history of mental health disorders) and uncovering new ones (i.e., help-seeking behavior and self-relationship, defined as self-directed emotional and cognitive patterns). Notably, as the frequency of NSSI increases, the significant influencing factors shift progressively from external to internal domains. CONCLUSION: This study provides valuable insights for the early detection and intervention of NSSI, helping to prevent its progression to PLFSA. Healthcare professionals can utilize these findings to inform personalized screening and intervention strategies for youth engaging in NSSI, with a focus on tailored mental health care and suicide prevention.

Authors

  • Yinan Duan
    Vanke School of Public Health, Tsinghua University, Beijing, 100084, China.
  • Chang Lei
    Vanke School of Public Health, Tsinghua University, Beijing, 100084, China.
  • Shicun Xu
    Northeast Asian Research Center, Jilin University, Changchun, China.
  • Juan Wang
    Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
  • Jingwen Li
    Cloud Computing and Big Data Research Institute, China Academy of Information and Communications Technology, People's Republic of China.
  • Yu Fu
    Molecular Diagnosis and Treatment Center for Infectious Diseases Dermatology Hospital Southern Medical University Guangzhou China.
  • Yingjun Xi
    Institute of Mental Health, Peking University, Beijing 100871, China; NHC Key Laboratory of Mental Health, Peking University, Beijing, 100871, China; National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, 100191, China. Electronic address: [email protected].
  • Jing An
    Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University Beijing, China.
  • Runsen Chen

Keywords

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