RESUMO
Objective@#Suicidal ideation (SI) precedes actual suicidal event. Thus, it is important for the prevention of suicide to screen the individualswith SI. This study aimed to identify the factors associated with SI and to build prediction models in Korean adults using machinelearning methods. @*Methods@#The 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 appliedand compared to the conventional logistic regression (LR)-based model. @*Results@#Common risk factors for SI included stress awareness, experience of continuous depressive mood, EQ-5D score, depressivedisorder, household income, educational status, alcohol abuse, and unmet medical service needs. The prediction performances of themachine learning models, as measured by the area under receiver-operating curve, ranged from 0.794 to 0.877, some of which were betterthan that of the conventional LR model (0.867). The Bayesian network, LogitBoost with LR, and ANN models outperformed the conventionalLR model. @*Conclusion@#A machine learning-based approach could provide better SI prediction performance compared to a conventional LRbasedmodel. These may help primary care physicians to identify patients at risk of SI and will facilitate the early prevention of suicide.Psychiatry Investig 2020;17(4):331-340