摘要
目的用机器学习算法建立IgA肾病与非IgA肾病鉴别诊断模型。方法采用回顾性研究的方法,收集2019至2020年昆明医科大学第一附属医院、云南省第一人民医院和昆明市延安医院肾脏内科经肾脏病理确诊的患者共260例,其中原发性IgA肾病130例,非IgA肾病130例。收集包括性别和年龄等在内的28项临床资料和实验室常规检测结果,IgA肾病组与非IgA肾病组的男女构成比分别为59∶71和64∶66,年龄分别为37.20(21.89,53.78)、43.30(27.77,59.18)岁。将260例患者随机地分为训练集(70%,182例)和测试集(30%,78例)。分别使用决策树、随机森林、支持向量机、极限梯度提升算法建立原发性IgA肾病与非IgA肾病的鉴别诊断模型。以真阳性率、真阴性率、假阳性率、假阴性率、准确率、受试者特征工作曲线下面积(AUC)、精确率、召回率和F1评分综合评估各模型的效能并选择性能最佳的模型。采用SPSS 25.0对数据进行分析,P<0.05为差异有统计学意义。结果采用决策树、支持向量机、随机森林和极限梯度提升算法建立鉴别诊断模型的准确度分别为67.95%、70.51%、80.77%和83.33%;AUC值分别0.74、0.76、0.80和0.83;判断为原发性IgA肾病的F1评分分别为0.73、0.72、0.80和0.83。综合以上评价指标极限梯度提升算法模型的效能最高,该模型诊断为IgA肾病的敏感度、特异度分别为89%、79%,其变量重要性由高到低分别为血白蛋白、IgA/C3、血肌酐、年龄、尿总蛋白、尿白蛋比、高密度脂蛋白、尿素。结论成功建立IgA肾病与非IgA肾病的鉴别诊断模型。采用极限梯度提升算法建立的模型临床性能最佳。
Objective To establish a differential diagnosis model for IgA nephropathy and non-IgA nephropathy based on machine learning algorithms.Methods Retrospective study adopted,from 2019 to 2020,260 patients were referred to the Department of Nephrology at the First Affiliated Hospital of Kunming Medical University,the First People′s Hospital in Yunnan province,and Yan′an Hospital of Kunming city.All patients were diagnosed by renal pathology,130 cases of primary IgA nephropathy,the 130 cases of non-IgA nephropathy.Collection of materials,including gender and age,28 clinical data,and routine laboratory test results,the sex ratio of IgA nephropathy group and non-IgA nephropathy group were 59∶71 and 64∶66 respectively,the ages were 37.20(21.89,53.78)and 43.30(27.77,59.18)years,respectively.260 patients were divided into a training set(70%,182 cases)and a test set(30%,78 cases).Using the decision tree,random forests,support vector machine,extreme gradient boosting to establish a differential diagnosis model for IgA nephropathy and non-IgA nephropathy.Based on the true positive rate,true negative rate,false-positive rate,false-negative rate,accuracy,subjects features work area under the curve(AUC),the precision ratio,recall ratio,and F1 score,comprehensively evaluate the performance of each model,finally,the best performance of the model was chosen.Using SPSS 25.0 to analyze the data,P<0.05 was considered to be statistically significant.Results The accuracy of the decision tree,support vector machine,random forests and extreme gradient boosting establish differential diagnosis model was 67.95%,70.51%,80.77%and 83.33%,respectively;AUC values was 0.74,0.76,0.80 and 0.83;Judgment for primary IgA nephropathy F1 score was 0.73,0.72,0.80 and 0.83,respectively.The efficiency of the extreme gradient boosting model based on the above evaluation indicators is the highest,its diagnosis of IgA nephropathy of the sensitivity and specificity respectively 89%and 79%.The variable importance from high to low was blood albumin,IgA/C
作者
杨晗
陈飞
陈浩
赵亮
张慧
刘记宏
刘子杰
Yang Han;Chen Fei;Chen Hao;Zhao Liang;Zhang Hui;Liu Jihong;Liu Zijie(Yunnan Key Laboratory of Laboratory Medicine,Kunming 650032,China;Department of Nephrology,the first people′s hospital in yunnan province,Kunming 650032,China;Department of Nephrology,Kunming yanan hospital,Kunming 650051,China;Department of Nephrology,the First Affiliated Hospital of Kunming Medical University,Kunming 650032,China)
出处
《中华检验医学杂志》
CAS
CSCD
北大核心
2022年第3期282-288,共7页
Chinese Journal of Laboratory Medicine
关键词
IGA肾病
非IgA肾病
机器学习算法
鉴别诊断
IgA nephropathy
Non-IgA nephropathy
Machine learning algorithms
Differential diagnosis
