Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study

ABSTRACT:

Background

Suicide remains the leading cause of premature death in South Korea. This study aims to develop machine learning algorithms for screening Korean adults at risk for suicidal ideation and suicide planning or attempt.

Methods

Two sets of balanced data for Korean adults aged 19–64 years were drawn from the 2012–2019 waves of the Korea Welfare Panel Study using the random down-sampling method (N = 3292 for the prediction of suicidal ideation, N = 488 for the prediction of suicide planning or attempt). Demographic, socioeconomic, and psychosocial characteristics were used to predict suicidal ideation and suicide planning or attempt. Four machine-learning classifiers (logistic regression, random forest, support vector machine, and extreme gradient boosting) were tuned

SUBMITTER: Lee J 

PROVIDER: S-EPMC9573904 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature