Research on the Construction of an Intelligent Syndrome Differentiation Model for Traditional Chinese Medicine in Lung Cancer Based on Ensemble Learning.

Corresponding author: YAN Junfeng, junfengyan@hnucm.edu.cn

DOI: 10.12201/bmr.202604.00093

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  Abstract: Abstract Objective/Significance To develop an intelligent syndrome differentiation model for lung cancer in traditional Chinese medicine (TCM))based on machine learning, and to provide methodological support for the structured utilization of TCM medical records and intelligent decision-making. Methods/Process Published TCM medical records of lung cancer up to September 30, 2025 were collected to construct a TCM lung cancer information database. The dataset was divided into training and testing sets at a ratio of 7:3. Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), CatBoost, and the constructed TCM-SAN (Traditional Chinese Medicine Symptom Attention Network) model were applied for syndrome differentiation modeling. Five-fold cross-validation was used to evaluate model performance, including accuracy, F1-score, recall, precision, and area under the curve (AUC). Results/Conclusion The TCM-SAN model outperformed other models in accuracy (86%), F1-score, and AUC, demonstrating robust performance and stability in four-diagnostic feature modeling and syndrome classification, offering a practical approach for intelligent TCM syndrome differentiation.

  Key words: Lung cancer; Traditional Chinese medicine syndrome differentiation; Machine learning; Structured four-diagnostic data; Explainable artificial intelligence.

  Submit time: 10 April 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2026-01-11	10.12201/bmr.202604.00093V1	Download

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