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Prediction of suicidal ideation in shift workers compared to non-shift workers using machine learning techniques.

Hwanjin Park¹, Kounseok Lee²

¹Department of Occupational & Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Journal of Affective Disorders

|April 7, 2022

Summary

Shift work significantly increases suicide risk, with shift workers being over twice as likely to experience suicidal ideation. Machine learning models accurately predicted these risks, highlighting depression and low quality of life as key factors.

Keywords:

Daytime workers Depression Machine learning Shift workers Suicide

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Area of Science:

Occupational Health
Psychiatry
Data Science

Background:

Shift work is linked to sleep disturbances and elevated suicide risk.
Predicting suicidal ideation in shift workers is crucial for targeted interventions.

Purpose of the Study:

To predict suicidal ideation among shift workers using machine learning.
To identify specific risk factors associated with suicidal ideation in different work schedules.

Main Methods:

Analysis of 43,095 participants from the Korean National Health and Nutrition Examination Survey (KHANES).
Application of machine learning techniques, specifically random forest (RF) and decision tree (DT), to categorize and analyze shift and daytime workers.
Evaluation of suicidal ideation rates based on work type, depression, and EuroQol-5 Dimension (EQ-5D) scores.

Main Results:

Shift workers demonstrated more than double the likelihood of suicidal ideation compared to daytime workers.
The RF model achieved high accuracy in predicting suicidal ideation (91.6% for shift workers, 98% for daytime workers).
The DT model identified high suicidal ideation rates (82.7%) among shift workers with depression and low EQ-5D scores (<0.71).

Conclusions:

Distinct variables influence suicide risk in shift and daytime workers.
Depression and reduced quality of life are significant risk factors for suicidal ideation in shift workers.
Administrative and policy interventions are necessary for early screening and management of shift worker health to mitigate suicide risk.