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| The study on the diagnosis model for post-stroke depression with the pattern of liver depression and spleen deficiency based on decision tree algorithm |
| Hits 163 Download times 25 Received:November 17, 2025 |
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| DOI
10.11656/j.issn.1672-1519.2026.03.04 |
| Key Words
post-stroke depression;the pattern of liver depression and spleen deficiency;decision tree;machine learning;diagnostic model;pattern diagnosis |
| Author Name | Affiliation | E-mail | | YU Ying | The Second Clinical Medical College, Beijing University of Chinese Medicine, Beijing 100029, China | | | HE Limin | The Second Clinical Medical College, Beijing University of Chinese Medicine, Beijing 100029, China | | | CHEN Yang | The Second Clinical Medical College, Beijing University of Chinese Medicine, Beijing 100029, China | | | YANG Weihao | The Second Clinical Medical College, Beijing University of Chinese Medicine, Beijing 100029, China | | | WANG Jialin | Department of Rehabilitation, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China | WJL2008420@163.com |
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| Abstract
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| [Objective] To establish a diagnostic model for post-stroke depression with the pattern of liver depression and spleen deficiency using decision tree algorithms，and to evaluate its diagnostic performance. [Methods] Clinical case data were collected to obtain basic patient information and data on traditional Chinese medicine syndrome patterns. Diagnostic models for post-stroke depression with the pattern of liver depression and spleen deficiency were constructed using the CART，CHAID，QUEST，and C5.0 decision tree algorithms. The performance of these models was then evaluated on an internal test set and an external validation dataset using metrics including accuracy and Area Under the Curve（AUC）. [Results] Diagnostic models were successfully established using the CART，CHAID，QUEST，and C5.0 decision tree algorithms. Among these，the C5.0 decision tree model demonstrated the best performance，achieving an accuracy of 90.91 percent and an AUC of 0.93. The symptoms of “distending pain in the hypochondria” “lack of energy and reluctance to speak” and “abdominal distension” were identified as the core classification features for diagnosing post-stroke depression with the pattern of liver depression and spleen deficiency. Among these，the symptom “lack of energy and reluctance to speak” exhibited the strongest discriminative ability for determining whether patients with post-stroke depression exhibited the pattern of liver depression and spleen deficiency. [Conclusion] The diagnostic model for post-stroke depression with the pattern of liver depression and spleen deficiency based on decision tree algorithms demonstrates high accuracy and can provide a reference for the diagnosis of post-stroke depression with the pattern of liver depression and spleen deficiency and the exploration of its clinical presentation patterns. |
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