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- W4382284740 abstract "The purpose of this study is a descriptive survey using data from the Korea national health and nutrition examination survey 2021 to identify factors that influence suicidal ideation in adults through machine learning. The subjects of the study were 208 adults with suicidal ideation and 4,449 adults without suicidal ideation, and machine learning was performed using a random forest model. Python 3.9.7, Scikit-learn 1.2.2, SciPy 1.614, and ELI5 packages were used for statistical analysis and model construction, and the mean, standard deviation, frequency, ratio, performance evaluation of the built model, and values of variables affecting suicidal thoughts were checked. The results showed that the average age of the subjects with suicidal ideation was 50.21±17.575 years, and their anxiety was 7.50±5.882, which was higher than that of adults without suicidal ideation (1.82±3.027). The overall performance of the constructed model was an AUC of 0.984 (95% CI, 0.984-0.984) and an accuracy of 0.945 (95% CI, 0.945-0.945). Factors influencing suicidal ideation were mainly related to mental health, with the most significant factor being depression for more than two weeks (0.998±0.038). Preemptive measures to prevent suicide by managing depression are needed, and programs to promote mental health and reduce psychological distress should be established." @default.
- W4382284740 created "2023-06-28" @default.
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- W4382284740 date "2023-06-30" @default.
- W4382284740 modified "2023-10-06" @default.
- W4382284740 title "Analysis of Influencing Factors of Suicide Ideation Using Random Forest Model : Focusing on the National Health and Nutrition Examination Survey" @default.
- W4382284740 doi "https://doi.org/10.37727/jkdas.2023.25.3.1121" @default.
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