Table 1_A preliminary study of a machine-learning prediction model for lung cancer diagnosis based on routine blood indicators and traditional chinese medicine diagnostic parameters.docx

Model performance in the validation cohort was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. SHAP analysis was applied to interpret feature contributions.</p>Results<p>LASSO identified 13 key variables, encompassing age, blood biochemical indicators, objective tongue features, and TCM constitution scores. The XGBoost model constructed based on these variables demonstrated good discriminative performance for lung cancer in the validation set (AUC = 0.8745, 95% CI: 0.8219–0.9271; F1 = 0.7711).

SHAP analysis revealed that age, absolute lymphocyte count, Qi-Deficiency Constitution Score (QDC-Score), apolipoprotein C3 (ApoC3), and tongue color features, including tongue coating color L* (TC-L) and full tongue color b* (FT-B), were the key factors driving model predictions.</p>Conclusion<p>We developed a low-cost screening model that integrates TCM-related features with routine clinical parameters, demonstrating good accuracy for identifying high-risk individuals for lung cancer among non-tumor populations.

This multimodal approach highlights the complementary value of integrating TCM indices with biomedical variables. Prospective multicenter validation is required to further optimize this model and facilitate its implementation in real-world clinical practice.</p>