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 and cross-validated.

Results: All four algorithms demonstrated satisfactory classification performance in predicting suicidal ideation (sensitivity 0.808-0.853, accuracy 0.843-0.863) and suicide planning or attempt (sensitivity 0.814-0.861, accuracy 0.864-0.884). Extreme gradient boosting was the best-performing algorithm for predicting both suicidal outcomes. The most important predictors were depressive symptoms, self-esteem, income, consumption, and life satisfaction. The algorithms trained with the top two predictors, depressive symptoms and self-esteem, showed comparable classification performance in predicting suicidal ideation (sensitivity 0.801-0.839, accuracy 0.841-0.846) and suicide planning or attempt (sensitivity 0.814-0.837, accuracy 0.874-0.884).

Limitations: Suicidal ideation and behaviors may be under-reported due to social desirability bias. Causality is not established.

Discussion: More than 80% of individuals at risk for suicidal ideation and suicide planning or attempt could be predicted by a number of mental and socioeconomic characteristics of respondents. This finding suggests the potential of developing a quick screening tool based on the known risk factors and applying it to primary care or community settings for early intervention.

Keywords: Machine learning; Predictive modeling; Self-harm; Suicidal ideation; Suicide planning.

Fig. 1

Recursive feature elimination results.

Fig. 2

Data construction and model development.