Ping Zhu
ABSTRACT
The objective is to investigate, using the random forest method, the factors that influence the psychological status of primary care practitioners in a professional medical environment. To rank and assess the significance of independent variables, a random forest model was used. Finally, a regression model was created based on the variables screened and logistic regression. During the period of newly developing infectious diseases, the majority of primary medical personnel experienced psychological issues. The PHQ-9 total score model of depression showed the smallest out-of-bag error rate when four independent factors were examined. When there were five independent components, the Anxiety screening Scale (GAD-7) total score model had the lowest out-of-bag error rate. The somatization Symptom Self-rating Scale (SSS) total score model exhibited the lowest out-of-bag error rate when there was only one independent variable. Those with high viral exposure were 4.35 times more likely to develop depression than those with low viral exposure, according to the PHQ-9 multivariate Logistic Regression model. Those with high viral exposure were 3,8 times more likely to develop generalized anxiety disorder than those with low viral exposure, according to a multivariate logistic regression model applying the Gad-7 scale. A multivariate logistic regression model of the SSS scale revealed that those with a bachelor's degree were 7.85 times more likely to acquire somatization symptoms than those with an associate's degree or less. Because it has a high reference value for relevance ranking and variable screening, evaluating data with the Random Forest model and Logistic Regression is an efficient process.
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Index Terms
- Analysis of public and mental health of medical staff in medical professional environment using random forest model
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