TY  - JOUR
PY  - 2023//
TI  - Predicting lifetime suicide attempts in a community sample of adolescents using machine learning algorithms
JO  - Assessment
A1  - Jankowsky, Kristin
A1  - Steger, Diana
A1  - Schroeders, Ulrich
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - Suicide is a major global health concern and a prominent cause of death in adolescents. Previous research on suicide prediction has mainly focused on clinical or adult samples. To prevent suicides at an early stage, however, it is important to screen for risk factors in a community sample of adolescents. We compared the accuracy of logistic regressions, elastic net regressions, and gradient boosting machines in predicting suicide attempts by 17-year-olds in the Millennium Cohort Study (N = 7,347), combining a large set of self- and other-reported variables from different categories. Both machine learning algorithms outperformed logistic regressions and achieved similar balanced accuracies (.76 when using data 3 years before the self-reported lifetime suicide attempts and.85 when using data from the same measurement wave). We identified essential variables that should be considered when screening for suicidal behavior. Finally, we discuss the usefulness of complex machine learning models in suicide prediction.<p />  <p>Language: en</p>
LA  - en
SN  - 1073-1911
UR  - http://dx.doi.org/10.1177/10731911231167490
ID  - ref1
ER  -