ArticlePsychiatry investigation2020
Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study.
Article in Psychiatry investigation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- Predicting Suicidal Ideation in Community-based Older Adults Using Self-report Questionnaires with Machine Learning.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025Article
- Optimizing Suicide Risk Prediction in Korea: A Comparison of Model Performance Using Resampling Methods and Machine Learning Algorithms.Psychiatry investigation · 2025Article
- Contribution of Chronic Disease in Predicting Depression and Suicidal Ideation Among the Older Adult Population.Psychiatry investigation · 2025Article
- Development of an AI-Based Suicide Ideation Prediction Model for People with Disabilities.Life (Basel, Switzerland) · 2024Article
- Do early-life circumstances predict late-life suicidal ideation? Evidence from SHARE data using machine learning.Frontiers in psychiatry · 2024Article
- Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.PeerJ. Computer science · 2024Article
- Factors Related to Suicidal Ideation by Gender and Age Group in Korean Adults.Journal of Korean medical science · 2023Article
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Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveSuicidal ideation (SI) precedes actual suicidal event. Thus, it is important for the prevention of suicide to screen the individuals with SI. This study aimed to identify the factors associated with SI and to build prediction models in Korean adults using machine learning methods.
methodsThe 2010-2013 dataset of the Korea National Health and Nutritional Examination Survey was used as the training dataset (n=16,437), and the subset collected in 2015 was used as the testing dataset (n=3,788). Various machine learning algorithms were applied and compared to the conventional logistic regression (LR)-based model.
resultsCommon risk factors for SI included stress awareness, experience of continuous depressive mood, EQ-5D score, depressive disorder, household income, educational status, alcohol abuse, and unmet medical service needs. The prediction performances of the machine learning models, as measured by the area under receiver-operating curve, ranged from 0.794 to 0.877, some of which were better than that of the conventional LR model (0.867). The Bayesian network, LogitBoost with LR, and ANN models outperformed the conventional LR model.
conclusionA machine learning-based approach could provide better SI prediction performance compared to a conventional LR-based model. These may help primary care physicians to identify patients at risk of SI and will facilitate the early prevention of suicide.
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Registered trials
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