{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T09:30:56Z","timestamp":1767087056640,"version":"3.48.0"},"reference-count":35,"publisher":"Korea Disease Control and Prevention Agency","issue":"6","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["ophrp.org"],"crossmark-restriction":true},"short-container-title":["PHRP"],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>Objectives: Although family members of persons with disabilities face elevated suicide risk, predictive models remain underdeveloped in Korean contexts. This study aimed to develop machine learning\u2013based predictive models for suicidal ideation among family members of persons with disabilities and examine differential risk patterns by disability onset type.Methods: This cross-sectional study analyzed 124,783 adult family members (59.9% spouses, 20.3% parents\/ascendants, 14.6% adult children, 5.2% extended family) from the 2018 Korean Disability and Life Dynamics Panel using survey weights. Four predictive models, including machine learning approaches, were compared using 31 variables. The dataset was divided into training (70%) and test (30%) sets, with stratified analyses comparing congenital and acquired disability groups.Results: Among the 124,783 family members analyzed, least absolute shrinkage and selection operator (LASSO) with cross-validation achieved optimal performance (area under the receiver operating characteristic curve, 0.875 training; 0.853 test). LASSO selected 16 of 31 variables for the total sample, with family members\u2019 depression as the strongest predictor (\u03b2=0.554), followed by disabled persons\u2019 suicidal ideation (\u03b2=0.425). Stratified LASSO analyses revealed that national basic livelihood beneficiary status was the strongest predictor for families with congenital disability (\u03b2=0.541), while family members\u2019 depression was the strongest predictor for families with acquired disability (\u03b2=0.562), demonstrating distinct predictive patterns by disability onset.Conclusion: 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.<\/jats:p>","DOI":"10.24171\/j.phrp.2025.0261","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T09:26:25Z","timestamp":1767086785000},"page":"560-574","update-policy":"https:\/\/doi.org\/10.24171\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning approach for predicting suicidal ideation among family members of persons with disabilities: a cross-sectional study in the Republic of Korea"],"prefix":"10.24171","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4940-4615","authenticated-orcid":true,"given":"Jin Hyuk","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"31909","reference":[{"key":"ref1","volume-title":"Suicide worldwide in 2019: global health estimates [Internet]","year":"2021"},{"key":"ref2","volume-title":"Health at a glance 2021 [Internet]","year":"2021"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.l94"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2023.115613"},{"key":"ref5","first-page":"55","volume-title":"The relationship between child\u2019s activities of daily living and depression in parents of children with developmental 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