TY  - JOUR
PY  - 2022//
TI  - A machine learning approach for predicting wage workers' suicidal ideation
JO  - Journal of personalized medicine
A1  - Park, Hwanjin
A1  - Lee, Kounseok
SP  - e945
EP  - e945
VL  - 12
IS  - 6
N2  - (1) Background: Workers spend most of their days working. One's working environment can be a risk factor for suicide. In this study, we examined whether suicidal ideation can be predicted using individual characteristics, emotional states, and working environments. (2) Methods: Nine years of data from the Korean National Health and Nutrition Survey were used. A total of 12,816 data points were analyzed, and 23 variables were selected. The random forest technique was used to predict suicidal thoughts. (3) Results: When suicidal ideation cases were predicted using all of the independent variables, 98.9% of cases were predicted, and 97.4% could be predicted using only work-related conditions. (4) Conclusions: It was confirmed that suicide risk could be predicted efficiently when machine learning techniques were applied using variables such as working environments.<p />  <p>Language: en</p>
LA  - en
SN  - 2075-4426
UR  - http://dx.doi.org/10.3390/jpm12060945
ID  - ref1
ER  -