@article{ref1,
title="A machine learning approach for predicting wage workers' suicidal ideation",
journal="Journal of personalized medicine",
year="2022",
author="Park, Hwanjin and Lee, Kounseok",
volume="12",
number="6",
pages="e945-e945",
abstract="(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>",
language="en",
issn="2075-4426",
doi="10.3390/jpm12060945",
url="http://dx.doi.org/10.3390/jpm12060945"
}