Establish a diagnosis differential model for IgA nephropathy and non-IgA nephropathy by machine learning
10.3760/cma.j.cn114452-20210510-00298
- VernacularTitle:用机器学习算法建立IgA肾病与非IgA肾病的鉴别诊断模型
- Author:
Han YANG
1
;
Fei CHEN
;
Hao CHEN
;
Liang ZHAO
;
Hui ZHANG
;
Jihong LIU
;
Zijie LIU
Author Information
1. 云南省检验医学重点实验室，昆明 650032
- Keywords:
IgA nephropathy;
Non-IgA nephropathy;
Machine learning algorithms;
Differential diagnosis
- From:
Chinese Journal of Laboratory Medicine
2022;45(3):282-288
- CountryChina
- Language:Chinese
-
Abstract:
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/C3, serum creatinine, age, urine protein, urine albumin, high-density lipoprotein cholesterol, urea.Conclusion:The differential diagnosis model for IgA nephropathy was established successfully and non-IgA nephropathy and the efficiency performance of the extreme gradient boosting algorithm was the best.
