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<p>Patients with infectious diseases are often at increased risk of anxiety during treatment. The prevalence of anxiety and depression in infected people increased significantly during the COVID-19 pandemic, and the risk factors for these mental health problems need to be urgently investigated. In this study, a cross-sectional study was conducted in Shanghai in 2022, which included 1283 patients and systematically assessed their sociodemographic characteristics and mental health status. A random forest classifier combined with the Boruta algorithm was used to screen predictors, and a nomogram was constructed based on the screening results. The results of the study showed that entrapment (OR 1.07, 95% CI 1.05–1.09, <i>P</i> &lt; 0.001), defeat (OR 1.04, 95% CI 1.01–1.07, <i>P</i> &lt; 0.01) and stigma (OR 1.05, 95% CI 1.03–1.06, <i>P</i> &lt; 0.001) were positively associated with anxiety, whereas social support (OR 0.97, 95% CI 0.96–0.98, <i>P</i> &lt; 0.001) was negatively associated with anxiety. The C-index of the model was 0.858, the area under the ROC curve (AUC) was 0.861 (95% CI 0.834–0.888), and the <i>P</i> value of the Hosmer–Lemeshow test was 0.07, indicating that the model fit well. Based on the Random Forest machine learning method, this study successfully constructed a prediction model for anxiety risk in COVID-19 patients, screening out key risk factors such as feeling trapped, frustration, stigma and social support, providing a scientific basis for clinical practice and public health, and helping to promote personalized interventions for anxiety and the building of a mental health support system.</p>

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Predicting factors associated with anxiety by patients undergoing treatment for infectious diseases using a random-forest machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Ruijie Chang,
  • Chenrui Li,
  • Dake Shi,
  • Fan Hu,
  • Yong Cai,
  • Ying Wang,
  • Tian Shen

摘要

Patients with infectious diseases are often at increased risk of anxiety during treatment. The prevalence of anxiety and depression in infected people increased significantly during the COVID-19 pandemic, and the risk factors for these mental health problems need to be urgently investigated. In this study, a cross-sectional study was conducted in Shanghai in 2022, which included 1283 patients and systematically assessed their sociodemographic characteristics and mental health status. A random forest classifier combined with the Boruta algorithm was used to screen predictors, and a nomogram was constructed based on the screening results. The results of the study showed that entrapment (OR 1.07, 95% CI 1.05–1.09, P < 0.001), defeat (OR 1.04, 95% CI 1.01–1.07, P < 0.01) and stigma (OR 1.05, 95% CI 1.03–1.06, P < 0.001) were positively associated with anxiety, whereas social support (OR 0.97, 95% CI 0.96–0.98, P < 0.001) was negatively associated with anxiety. The C-index of the model was 0.858, the area under the ROC curve (AUC) was 0.861 (95% CI 0.834–0.888), and the P value of the Hosmer–Lemeshow test was 0.07, indicating that the model fit well. Based on the Random Forest machine learning method, this study successfully constructed a prediction model for anxiety risk in COVID-19 patients, screening out key risk factors such as feeling trapped, frustration, stigma and social support, providing a scientific basis for clinical practice and public health, and helping to promote personalized interventions for anxiety and the building of a mental health support system.