采用机器学习技术建立布鲁杆菌病早期预测模型

Development of auxiliary early predicting model for human brucellosis using machine learning algorithm

采用机器学习技术构建布鲁杆菌病(简称:布病)早期预测模型,以辅助提高布病的诊断效率。本文为病例对照研究,收集2011年5月9日至2021年11月29日首都医科大学附属北京地坛医院的布病患者2 381例作为病例组,首都医科大学附属北京朝阳医院表观健康人检验数据13 257例作为对照组。采用患者年龄、性别、临床诊断信息及22项血细胞分析结果,使用机器学习的随机森林、朴素贝叶斯、决策树、逻辑回归和支持向量机5种算法构建布病早期预测模型;其中14 074例(病例组2 143例,对照组11 931名)用于构建布病早期预测模型,1 564例(病例组238例,对照组1 326名)用于测试模型的预测效能。结果显示,通过对5种机器学习模型进行比对,支持向量机模型预测性能最佳,受试者工作曲线(ROC)线下面积(AUC)为0.991,准确度、精确度、特异度和召回率分别可达95.6%、95.5%、95.4%和95.9%。依据SHAP图显示,血小板分布宽度(PDW)和嗜碱粒细胞相对值(BASO%)结果较低,红细胞分布宽度变异系数(R-CV)、红细胞血红蛋白浓度(MCHC)和血小板体积(MPV)结果高的男性被预测为布病风险高。其中,血小板分布宽度(PDW)对预测模型贡献度最大,红细胞分布宽度变异系数(R-CV)次之。综上,基于机器学习技术建立高灵敏度的布病早期预测方法,对布病患者的及早发现、尽快治疗可能具有重要意义。

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis.This study was a case-control study.2381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group,and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9,2011 to November 29,2021.The relevant clinical information and full blood count results of 13257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning:random forest,Naive Bayes,decision tree,logistic regression and support vector machine;14074 data(2143 cases incase group and 11931 cases in control group)were used to establish the early predication model of brucellosis,and 1564(238 cases in case group and 1326 cases in control group)data were used to test the predication efficiency of the brucellosis model.The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models.The area under receiver curve(AUC)of receiver operating characteristic(ROC)was 0.991,and the accuracy,precision,specificity and Recall were 95.6%,95.5%,95.4%and 95.9%,respectively.Based on the SHAP plot,platelet distribution width(PDW)and basophil relative value(BASO%)results were low,and men with high coefficient of variation(R-CV),erythrocyte hemoglobin concentration(MCHC),and platelet volume(MPV)were predicted to be at high risk of brucellosis.Platelet distribution width(PDW)contributed the most to the prediction model,followed by red blood cell distribution width coefficient of variation(R-CV).In conclusion,the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.