临床与病理杂志2025，Vol.45Issue(1)：25-37,13.DOI:10.11817/j.issn.2095-6959.2025.241054
基于机器学习优化的血液指标肺结核诊断模型:多中心研究
Blood-based diagnostic model for pulmonary tuberculosis optimized by machine learning:A multicenter study
摘要
Abstract
Objective:Pulmonary tuberculosis is a serious infectious disease worldwide,and accurate and rapid diagnosis is crucial for reducing transmission and optimizing treatment.Existing diagnostic methods are limited in resource-constrained regions due to high costs,making the development of an economical and efficient blood-based diagnostic tool highly significant. Methods:This study included 121 tuberculosis patients(tuberculosis group)and 101 healthy controls(healthy control group).Blood indicators were analyzed statistically to identify features significantly associated with tuberculosis.Three machine learning algorithms,eXtreme Gradient Boosting(XGBoost),Support Vector Machine Recursive Feature Elimination(SVM-RFE),and Boruta,were used for feature selection.Various machine learning models were constructed using the selected features.The Shapley Additive Explanations(SHAP)method was used to explain the importance and contribution of model features,further analyzing the mechanism of action of features and their impact on classification performance. Results:Several blood indicators significant differed between the tuberculosis group and the healthy control group,including lymphocyte percentage(LYM%),eosinophil percentage(EOS%),aspartate aminotransferase(AST),eosinophil percentage(EOS#),and neutrophil absolute count(NEU#).XGBoost selected 34 key features,SVM-RFE performed best with 5 features,and Boruta identified 15 significant features.The intersection of the 3 methods contained 5 core features(LYM%,EOS%,AST,EOS#,NEU#).In model development,XGBoost achieved areas under the receiver operating characteristic curve of 0.989,0.975,and 0.969 for the training,validation,and external validation groups,respectively,with a validation accuracy rate of 94%,showing optimal performance.SHAP analysis further confirmed that LYM%made a significant positive contribution to model,while AST and EOS#had negative contributions,and significant interactions between features and observed. Conclusion:This study successfully developed an efficient tuberculosis diagnostic model by integrating blood indicators and machine learning algorithms,offering high accuracy and good generalization ability.Compared to existing diagnostic methods,the model,based on routine laboratory indicators,is cost-effective and easy to implement,especially in resource-limited areas.关键词
机器学习/血液指标/诊断模型/肺结核/治疗优化Key words
machine learning/blood indicators/diagnostic model/pulmonary tuberculosis/optimizing treatment引用本文复制引用
周靖,丁寿鹏,蔡子涵..基于机器学习优化的血液指标肺结核诊断模型:多中心研究[J].临床与病理杂志,2025,45(1):25-37,13.基金项目
泗阳医院与江苏大学附属医院第一届院内科研计划项目(2024SY005).This work was supported by the First Intra-hospital Research Plan Project of Siyang Hospital and Jiangsu University Affiliated Hospital(2024SY005),China. （2024SY005）
