Investigation of Machine Learning Models for Anxiety Levels Assessment in Patients With Breast Cancer Using Self-Questionnaires.

BACKGROUND

This study aimed to develop a machine-learning model using self-report questionnaires to screen for clinically significant anxiety symptoms in patients with breast cancer experiencing severe distress.

METHODS

A cohort of 327 breast cancer clinic patients was included. Anxiety symptoms were evaluated using the State-Trait Anxiety Inventory-State (STAI-S), State-Trait Anxiety Inventory-Trait (STAI-T), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale (HADS) questionnaires. The high-risk anxiety group was determined based on the Mini International Neuropsychiatric Interview Patient Health Survey. Supervised machine learning models were analyzed and validated using MATLAB2022.

RESULTS

The BAI showed an area under the curve (AUC) of 0.782 with the logistic regression classifier. The HADS showed an AUC of 0.784 with linear discriminant analysis. STAI-S and STAI-T exhibited AUCs of 0.770 and 0.791, respectively, using Support Vector Machine with a linear kernel. Models combining multiple questionnaires yielded higher AUCs: STAI-S and STAI-T combined resulted in 0.807, STAI-S and BAI in 0.794, STAI-T and BAI in 0.808, and the combination of STAI-S, STAI-T, and BAI achieved 0.810.

CONCLUSION

The combination models demonstrated superior performance in detecting anxiety among patients with breast cancer compared to individual questionnaires. Combining multiple self-report scales enhances screening accuracy, indicating future research should optimize these models further through integration with other methodologies.