Ogawa S. Predicting Dropout From Cognitive Behavioral Therapy for Panic Disorder Using Machine Learning Algorithms.
J Clin Med Res 2024;
16:251-255. [PMID:
38855785 PMCID:
PMC11161187 DOI:
10.14740/jocmr5167]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024] Open
Abstract
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.
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