【摘要】 目的：构建癌因性疲乏（CRF）气血两虚证人群的分类预测模型。方法：以2019年10月至2022年4月期间全国29家分中心符合纳入标准的CRF气血两虚证患者为研究对象，基于决策树算法构建人群分类预测模型。结果：建模共纳入591例患者信息，将Piper疲乏调查量表（PFS）总均分、血常规指标、CRF气血两虚证诊断症状纳入决策树模型，其中外周血红细胞计数（RBC）、外周血白细胞计数（WBC）水平对人群划分起主要决定作用，经测试模型预测准确率为82.49%；仅纳入单一变量不适合使用决策树算法构建CRF气血两虚证人群分类预测模型。结论：基于决策树算法构建的CRF气血两虚证人群分类预测模型，结合宏观、微观指标进行人群富集分类，具有较为良好的性能。更多还原

【Abstract】 Objective: To construct a classification prediction model for cancer-related fatigue(CRF) patients with qi and blood deficiency syndrome. Methods: CRF patients with qi and blood deficiency syndrome from 29sub-centers nationwide(October 2019 to April 2022) were included. A population classification prediction model was constructed based on the decision tree algorithm. Results: Data from 591 patients were incorporated into the model, including the mean score of the Piper Fatigue Scale(PFS), blood routine examination indicators, and TCM diagnostic symptoms for qi and blood deficiency syndrome. Red blood cell(RBC) count and white blood cell(WBC) count were identified as key predictors for population classification. The model’s prediction accuracy reached 82.49%. Relying on a single type of variable was found unsuitable for constructing an effective classification model using the decision tree algorithm. Conclusion: The decision tree-based classification prediction model for CRF patients with qi and blood deficiency syndrome demonstrates relatively good performance by combining macro(symptom) and micro(laboratory) indicators for effective population enrichment and classification.更多还原