Optimizing Suicide Risk Prediction in Korea: A Comparison of Model Performance Using Resampling Methods and Machine Learning Algorithms
Developing an optimized Korean suicide prediction model through additional validation based on the ML model developed in this study will help predict suicide risk factors caused by the interaction of individual, social, and environmental factors.
Authors
- Sung‐Hyo Seo (ORCID: https://orcid.org/0000-0003-3327-5425)
- Eunji Lim (ORCID: https://orcid.org/0000-0003-3967-8524)
- Boseok Cha (ORCID: https://orcid.org/0000-0002-3309-8863)
- Jaewon Choi (ORCID: https://orcid.org/0000-0001-9795-553X)
- Nuree Kang (ORCID: https://orcid.org/0000-0001-7630-4033)
- Soyoung Park (ORCID: https://orcid.org/0000-0001-6435-9416)
- Bong‐Jo Kim (ORCID: https://orcid.org/0000-0002-3722-2032)
- Dongyun Lee (ORCID: https://orcid.org/0000-0002-3977-3663)
- So-Jin Lee
Institutions
- Changwon National University (KR)
- Gyeongsang National University (KR)
- Gyeongsang National University Hospital (KR)
Publication Details
- Journal
- Psychiatry Investigation
- Published
- 2025-11-13
- DOI
- https://doi.org/10.30773/pi.2025.0187
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Gyeongsang National University