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Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.
- Source :
- Healthcare (2227-9032); Jul2024, Vol. 12 Issue 13, p1262, 15p
- Publication Year :
- 2024
-
Abstract
- Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment. [ABSTRACT FROM AUTHOR]
- Subjects :
- SUICIDE risk factors
RISK assessment
SEASONAL affective disorder
SUICIDAL ideation
PREDICTION models
RECEIVER operating characteristic curves
MENTAL health
QUESTIONNAIRES
LOGISTIC regression analysis
RESEARCH evaluation
HUMAN sexuality
DESCRIPTIVE statistics
SUICIDE prevention
SUPPORT vector machines
SEX customs
DESPAIR
MACHINE learning
CHILDHOOD obesity
MENTAL depression
EVALUATION
ADOLESCENCE
Subjects
Details
- Language :
- English
- ISSN :
- 22279032
- Volume :
- 12
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- Healthcare (2227-9032)
- Publication Type :
- Academic Journal
- Accession number :
- 178691001
- Full Text :
- https://doi.org/10.3390/healthcare12131262