Predicting Dropout From Cognitive Behavioral Therapy for Panic Disorder Using Machine Learning Algorithms

Sei Ogawa


Background: Attrition is an important problem in clinical practice and research. However, the predictors of dropping out from cognitive behavioral therapy (CBT) for panic disorder (PD) are not fully understood. In this study, we aimed to build a dropout prediction model for CBT for PD using machine learning (ML) algorithms.

Methods: We treated 208 patients with PD applying group CBT. From baseline data, the prediction analysis was carried out using two ML algorithms, random forest and light gradient boosting machine. The baseline data included five personality dimensions in NEO Five Factor Index, depression subscale of Symptom Checklist-90 Revised, age, sex, and Panic Disorder Severity Scale.

Results: Random forest identified dropout during CBT for PD showing that the accuracy of prediction was 88%. Light gradient boosting machine showed that the accuracy was 85%.

Conclusions: The ML algorithms could detect dropout after CBT for PD with relatively high accuracy. For the purpose of clinical decision-making, we could use this ML method. This study was conducted as a naturalistic study in a routine clinical setting. Therefore, our results in ML approach could be generalized to regular clinical settings.

J Clin Med Res. 2024;16(5):251-255


Panic disorder; Cognitive behavioral therapy; Machine learning; Predictor

Full Text: HTML PDF

Browse  Journals  


Journal of Clinical Medicine Research

Journal of Endocrinology and Metabolism

Journal of Clinical Gynecology and Obstetrics


World Journal of Oncology

Gastroenterology Research

Journal of Hematology


Journal of Medical Cases

Journal of Current Surgery

Clinical Infection and Immunity


Cardiology Research

World Journal of Nephrology and Urology

Cellular and Molecular Medicine Research


Journal of Neurology Research

International Journal of Clinical Pediatrics



Journal of Clinical Medicine Research, monthly, ISSN 1918-3003 (print), 1918-3011 (online), published by Elmer Press Inc.                     
The content of this site is intended for health care professionals.
This is an open-access journal distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License, which permits unrestricted
non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons Attribution license (Attribution-NonCommercial 4.0 International CC-BY-NC 4.0)

This journal follows the International Committee of Medical Journal Editors (ICMJE) recommendations for manuscripts submitted to biomedical journals,
the Committee on Publication Ethics (COPE) guidelines, and the Principles of Transparency and Best Practice in Scholarly Publishing.

website:   editorial contact:
Address: 9225 Leslie Street, Suite 201, Richmond Hill, Ontario, L4B 3H6, Canada

© Elmer Press Inc. All Rights Reserved.

Disclaimer: The views and opinions expressed in the published articles are those of the authors and do not necessarily reflect the views or opinions of the editors and Elmer Press Inc. This website is provided for medical research and informational purposes only and does not constitute any medical advice or professional services. The information provided in this journal should not be used for diagnosis and treatment, those seeking medical advice should always consult with a licensed physician.