A machine learning approach for predicting suicidal ideation among family members of persons with disabilities: a cross-sectional study in the Republic of Korea

These findings show that predictive factors differ substantially by disability onset type, indicating the need for tailored intervention approaches and offering an evidence-based foundation for targeted suicide prevention strategies.

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Publication Details

Journal
Osong Public Health and Research Perspectives
Published
2025-12-30
DOI
https://doi.org/10.24171/j.phrp.2025.0261
Primary Topic
Suicide and Self-Harm Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

A machine learning approach for predicting suicidal ideation among family members of persons with disabilities: a cross-sectional study in the Republic of Korea

Jin Hyuk Lee
Osong Public Health and Research Perspectives
Suicide and Self-Harm Studies
article

A machine learning approach for predicting suicidal ideation among family members of persons with disabilities: a cross-sectional study in the Republic of Korea

Jin Hyuk Lee
article en

Abstract

These findings show that predictive factors differ substantially by disability onset type, indicating the need for tailored intervention approaches and offering an evidence-based foundation for targeted suicide prevention strategies.

Osong Public Health and Research PerspectivesVol. 16(6)
Baekseok Culture University (KR), Baekseok University (KR)
Openalex Percentile: Top 48%
Suicide and Self-Harm Studies
DOIOpen Access PDFOpenAlex
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A machine learning approach for predicting suicidal ideation among family members of persons with disabilities: a cross-sectional study in the Republic of Korea — Jin Hyuk Lee · Osong Public Health and Research Perspectives (2025) | TGRS Research Map | TGRS