Clinical Psychopharmacology and Neuroscience Papers in Press available online.

 
Exploration of a machine learning model using self-rating questionnaires for detecting depression in patients with breast cancer
Heeseung Park 1,2, Kyungwon Kim 2,3, Eunsoo Moon 2,3,4,*, Hyun Ju Lim 2,3,5, Hwagyu Suh 2,3, Kyoung-Eun Kim 1,2, Taewoo Kang 1,2
1Breast Cancer Clinic of Busan Cancer Center, Pusan National University Hospital, Busan, Republic of Korea, 2Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea, 3Department of Psychiatry, Pusan National Universit Hospital, Busan, Republic of Korea, 4Department of Psychiatry, Pusan National University School of Medicine, Yangsan, Republic of Korea, 5Department of Psychology, Gyeoungsang National University, Jinju, Republic of Korea
Abstract
Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer.
Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders. The prediction model's performance based on supervised machine learning was conducted using MATLAB2022.
Results: The BDI showed an AUC of 0.785 when using the Logistic Regression (LR) classifier. The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the Linear Discriminant Analysis, respectively. The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR.
Conclusion: The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.
Accepted Manuscript [Submitted on 2023-11-16, Accepted on 2024-02-26]