Comparison of Machine Learning Models for Predicting Suicide Attempts among Korean Adolescents with Suicidal Ideation: Secondary Data Analysis Based on 20th Korea Youth Risk Behavior Survey.
Purpose: This study aimed to identify the factors associated with the progression from suicidal ideation to suicide attempts and to compare the predictive performance of various machine learning models. Methods: We conducted a secondary analysis using original data from the 20th Korea Youth Risk Beh...
| Publicado en: | Journal of Korean Academy of Psychiatric & Mental Health Nursing (JKPMHN) Vol. 34; pp. 57 - 69 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Korean Academy of Psychiatric & Mental Health Nursing
2025 Special Issue
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190360984&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190360984 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 12258482 HC6L jtl: Journal of Korean Academy of Psychiatric & Mental Health Nursing (JKPMHN) issn: 12258482 maglogo: N pubinfo: dt: 2025 Special Issue vid: 34 pid: 93726 pub: Korean Academy of Psychiatric & Mental Health Nursing artinfo: ui: 190360984 190360984 190360984 10.12934/jkpmhn.2025.34.S1.57 190360984 ppf: 57 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparison of Machine Learning Models for Predicting Suicide Attempts among Korean Adolescents with Suicidal Ideation: Secondary Data Analysis Based on 20th Korea Youth Risk Behavior Survey. aug: au: Jang, Yang-Min Heo, Myoung-Lyun affil: Assistant Professor, Department of Nursing, Cheongju university, Cheongju, Korea sug: subj: Machine Learning Algorithms Prediction Models Suicide Prevention Suicide, Attempted Prevention and Control Suicidal Ideation Risk Factors Adolescent Behavior South Korea Risk Assessment Human Male Female Child Adolescence South Korea Secondary Analysis Prospective Studies Suicide, Attempted Epidemiology Suicidal Ideation Epidemiology Random Forest Logistic Regression Violence Self-Injurious Behavior Depression Complications Anxiety Complications Stress Disorders, Post-Traumatic Complications Stress, Psychological Family Relations Support, Social Substance Use Disorders Epidemiology Self Administration Questionnaires Descriptive Statistics Comparative Studies Summated Rating Scaling Coefficient alpha Data Analysis Software Chi Square Test T-Tests ROC Curve Correlation Coefficient Probability Odds Ratio Confidence Intervals Community Mental Health Nursing Child: 6-12 years Adolescent: 13-18 years Male Female ab: Purpose: This study aimed to identify the factors associated with the progression from suicidal ideation to suicide attempts and to compare the predictive performance of various machine learning models. Methods: We conducted a secondary analysis using original data from the 20th Korea Youth Risk Behavior Survey (KYRBS), focusing on 6,316 adolescents who reported suicidal ideation. We evaluated predictive performance using logistic regression, random forest, and k-nearest neighbors (KNN) models. Results: Suicide attempts were significantly associated with sociodemographic factors, such as academic achievement, economic status, type of residence, and perceived health status, as well as psychological and behavioral factors, including suicidal planning, feelings of sadness and despair, anxiety, alcohol use, smoking, drug use, and exposure to violence. Logistic regression exhibited the highest predictive performance (AUC=0.77, accuracy=0.84, F1=0.80). The random forest model identified suicidal planning, loneliness, generalized anxiety, drug use, and exposure to violence as key predictors based on the Gini index, while KNN demonstrated the lowest predictive stability. Conclusion: Logistic regression is effective for predicting suicide attempts among adolescents, and machine learning approaches should be considered for early risk screening in community mental health nursing. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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