Exploration of a Machine Learning Model Using Self-rating Questionnaires for Detecting Depression in Patients with Breast Cancer
Heeseung Park1,2,3, Kyungwon Kim2,4,5, Eunsoo Moon2,4,5, Hyun Ju Lim2,4,6, Hwagyu Suh2,4,5, Kyoung-Eun Kim1,2, Taewoo Kang1,2,3
1Breast Cancer Clinic of Busan Cancer Center, Pusan National University Hospital, Busan, Korea
2Biomedical Research Institute, Pusan National University Hospital, Busan, Korea
3Department of Surgery, Pusan National University School of Medicine, Busan, Korea
4Department of Psychiatry, Pusan National University Hospital, Busan, Korea
5Department of Psychiatry, Pusan National University School of Medicine, Yangsan, Korea
6Department of Psychology, Gyeoungsang National University, Jinju, Korea
Correspondence to: Eunsoo Moon
Department of Psychiatry and Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, 179 Gudeok-ro, Seo-gu, Busan 49241, Korea
E-mail: esmun@hanmail.net
ORCID: https://orcid.org/0000-0002-8863-3413

Taewoo Kang
Department of Surgery (Busan Cancer Center) and Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, 179 Gudeok-ro, Seo-gu, Busan 49241, Korea
E-mail: taewoo.d.kang@gmail.com
ORCID: https://orcid.org/0000-0002-6279-0904
Received: December 6, 2023; Revised: February 7, 2024; Accepted: February 26, 2024; Published online: March 20, 2024.
© The Korean College of Neuropsychopharmacology. All rights reserved.

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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 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022.
Results: The BDI showed an area under the curve (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.
Keywords: Breast neoplasms; Depression; Machine learning; Self report


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