| 引用本文: |
魏方志, 潘承丹, 宋逸天, 庄燕苹, 张绚, 曾旻昱, 贾晓康, 宫爱民.基于XGBoost算法的系统性红斑狼疮中医证型判别模型研究[J].湖南中医药大学学报,2024,44(12):2286-2293[点击复制] |
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| 基于XGBoost算法的系统性红斑狼疮中医证型判别模型研究 |
| 魏方志,潘承丹,宋逸天,庄燕苹,张绚,曾旻昱,贾晓康,宫爱民 |
| (海南医科大学(海南医学科学院)中医学院, 海南 海口 571199;博鳌一龄生命养护中心, 海南 琼海 571400) |
| 摘要: |
| 目的 通过XGBoost算法构建系统性红斑狼疮（systemic lupus erythematosus,SLE）中医证型判别模型，探索XGBoost模型用于证型分类的可行性。方法 通过问卷调查法，收集符合标准的病例，建立SLE数据集。通过XGBoost算法构建SLE中医证型判别模型，采用随机森林（random forest,RF）算法作为对照，比较两种算法的准确性。结果 本硏究共纳入400例SLE患者，其中男性33例，女性367例。SLE患者排名前3的中医证型为：脾肾阳虚证、阴虚内热证和风湿热痹证，XGBoost算法模型分类指标和性能曲线评分总体优于RF算法。结论 XGBoost算法用于证候建模准确度较高，可用于证候研究中的分类研究。 |
| 关键词: 系统性红斑狼疮 XGBoost算法 随机森林算法 中医证候 |
| DOI：10.3969/j.issn.1674-070X.2024.12.021 |
| 投稿时间：2024-05-22 |
| 基金项目:国家自然科学基金项目（30109065）。 |
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| Chinese medicine pattern differentiation model for systemic lupus erythematosus based on XGBoost algorithm |
| WEI Fangzhi, PAN Chengdan, SONG Yitian, ZHUANG Yanping, ZHANG Xuan, ZENG Minyu, JIA Xiaokang, GONG Aimin |
| (School of Chinese Medicine, Hainan Medical University (Hainan Academy of Medical Sciences), Haikou, Hainan 571199, China;Bo'ao Yiling Life Care Center, Qionghai, Hainan 571400, China) |
| Abstract: |
| Objective To construct a Chinese medicine (CM) pattern differentiation model for systemic lupus erythematosus (SLE) using the XGBoost algorithm and explore the feasibility of applying the XGBoost model for CM pattern classification. Methods Eligible cases were collected through a questionnaire survey to establish a SLE dataset. An XGBoost-based SLE CM pattern differentiation model was developed, and the random forest (RF) algorithm was used as a control for accuracy comparison. Results A total of 400 SLE patients were included in this study, including 33 males and 367 females. The top three CM patterns for SLE patients were yang deficiency of the spleen and kidney pattern, yin deficiency-induced internal heat pattern, and wind dampness and heat impediment pattern. The classification indicators and performance curve scores of the XGBoost algorithm model were overall superior to those of the RF algorithm. Conclusion XGBoost algorithm demonstrates high accuracy in CM pattern modeling and can be used for classification research in CM pattern studies. |
| Key words: systemic lupus erythematosus XGBoost algorithm random forest algorithm Chinese medicine pattern |
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