Three predictive models for fear of cancer recurrence among recovering breast cancer patients
Abstract
Objectives: Fear of cancer recurrence (FCR) is a common psychological issue among breast cancer patients, yet predictive methods are lacking. This study aimed to construct and compare three models for predicting FCR among recovering breast cancer patients. Methods: A total of 490 breast cancer patients in rehabilitation were recruited from a university hospital in Harbin, China. Factors affecting FCR were collected using specific questionnaires. Multivariate analysis identified effective influencing factors, which were used as independent variables in a logistic regression model, an artificial neural network (ANN) model, and a random forest model. The dependent variable was high-level FCR. Models were validated using 10 × 10 cross-validation, and their performance was compared using AUC values, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results: Six factors influenced FCR: age, occupational status, chemotherapy, self-image, cancer-caused fatigue, and disease perception. The logistic regression model showed an accuracy of 83.7%, sensitivity of 79.3%, specificity of 86.9%, positive predictive value of 85.1%, negative predictive value of 81.7%, and an AUC value of 0.746. The ANN model had an accuracy of 78.2%, sensitivity of 80.0%, specificity of 75.8%, positive predictive value of 81.9%, negative predictive value of 73.4%, and an AUC value of 0.746. The random forest model demonstrated an accuracy of 84.4%, sensitivity of 89.4%, specificity of 77.4%, positive predictive value of 84.4%, negative predictive value of 84.2%, and an AUC value of 0.892. Conclusion: The random forest model outperformed the ANN and logistic regression models in predicting FCR in recovering breast cancer patients, making it a valuable tool for screening high-risk FCR groups during rehabilitation.
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