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Journal of Southern Medical University ›› 2026, Vol. 46 ›› Issue (7): 1660-1670.doi: 10.12122/j.issn.1673-4254.2026.07.19

Previous Articles    

Construction of an anxiety recognition model for patients with malignant gastrointestinal tumors based on multidimensional features and multiple interpretable machine learning

Mengmeng CHEN1(), Runfang GUO4, Wenjie ZHAO4, Yongqi WANG1, Lugen ZUO2,3, Mulin LIU2(), Bin LI1,4,5()   

  1. 1.School of Medical Information and Engineering, Hospital Management Research Institute, Bengbu Medical University, Bengbu 233030, China
    2.School of Public Health, Hospital Management Research Institute, Bengbu Medical University, Bengbu 233030, China
    3.Institute of Digital Healthcare, Hospital Management Research Institute, Bengbu Medical University, Bengbu 233030, China
    2.Department of Gastrointestinal Surgery, First Affiliated Hospital of Bengbu Medical University, Bengbu 233004, China
    3.Anhui Provincial Key Laboratory of Basic and Translational Research on Inflammation-Related Diseases, Bengbu 233004, China
  • Received:2025-12-17 Online:2026-07-20 Published:2026-07-20
  • Contact: Mulin LIU, Bin LI E-mail:20231002151@stu.bbmu.edu.cn;liumulin66@aliyun.com;libin@bbmu.edu.cn

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Abstract:

Objective To explore the risk factors for concomitant anxiety in patients with malignant gastrointestinal tumors based on machine learning algorithms. Methods A total of 280 patients with malignant gastrointestinal tumors admitted to the First Affiliated Hospital of Bengbu Medical University from November, 2022 to April, 2023 were enrolled, and the collected data were re-analyzed between November,2024 and April, 2025. According to Hospital Anxiety and Depression Scale (HADS-A) anxiety scores, the patients were divided into anxiety group (HADS-A score≥8) and non-anxiety group (<8). Six machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine and Light Gradient Boosting Machine were used to predict anxiety. The performance of each model was assessed, and the factors affecting anxiety in these patients were identified by feature importance ranking and SHAP analysis by the optimal model. Results Among the 6 machine learning algorithms for predicting anxiety in patients with malignant gastrointestinal tumors, the Random Forest model showed the best performance with an accuracy of 0.73, precision of 0.48, recall of 0.86, specificity of 0.69, F1-Score of 0.62, and the area under the ROC curve (AUC) of 0.85, and was more accurate for identifying and classifying anxiety status in the target patients. According to the feature importance ranking by the optimal model, the top 10 key factors affecting anxiety in these patients were total cholesterol (0.0406), gender (0.0224), albumin (0.0157), platelet count (0.0152), white blood cell count (0.0121), retinol-binding protein (0.0116), total protein (0.0115), body mass index (0.0098), C-reactive protein (0.0096), and platelet distribution width (0.0095). Conclusion The Random Forest model has good performance for predicting anxiety and screening key factors affecting anxiety in patients with malignant gastrointestinal tumors to facilitate its early identification and targeted intervention.

Key words: gastrointestinal malignant tumors, anxiety, machine learning, influencing factors