Decoding vital variables in predicting different phases of suicide among young adults with childhood sexual abuse: a machine learning approach.

ABSTRACT: Young adults with childhood sexual abuse (CSA) are an especially vulnerable group to suicide. Suicide encompasses different phases, but for CSA survivors the salient factors precipitating suicide are rarely studied. In this study, from a progressive perspective of suicidal thoughts and behaviors (STB), we aim to identify distinct risk factors for predicting different stages of STB, i.e., suicidal ideation (SI), suicide plan (SP), and suicide attempt (SA), among young adults with CSA experience. Based on mental health profiles of 4,070 young adult CSA survivors from a cross-sectional survey, we constructed five random forest classification models to respectively classify high suicidality, SI, SP, and SA. The common crucial factors for predicting SI, SP, and SA included NSSI and depression.

SUBMITTER: Niu W 

PROVIDER: S-EPMC12022041 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature