Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms
Context Four algorithms with relatively balanced complexity and accuracy in deep learning classification algorithm were selected for differential diagnosis of primary membranous nephropathy (PMN).Objective This study explored the most suitable classification algorithm for PMN identification, and to...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2380752 |
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| author | Shangmei Cao Shaozhe Yang Bolin Chen Xixia Chen Xiuhong Fu Shuifu Tang |
| author_facet | Shangmei Cao Shaozhe Yang Bolin Chen Xixia Chen Xiuhong Fu Shuifu Tang |
| author_sort | Shangmei Cao |
| collection | DOAJ |
| description | Context Four algorithms with relatively balanced complexity and accuracy in deep learning classification algorithm were selected for differential diagnosis of primary membranous nephropathy (PMN).Objective This study explored the most suitable classification algorithm for PMN identification, and to provide data reference for PMN diagnosis research.Methods A total of 500 patients were referred to Luo-he Central Hospital from 2019 to 2021. All patients were diagnosed with primary glomerular disease confirmed by renal biopsy, contained 322 cases of PMN, the 178 cases of non-PMN. Using the decision tree, random forest, support vector machine, and extreme gradient boosting (Xgboost) to establish a differential diagnosis model for PMN and non-PMN. Based on the true positive rate, true negative rate, false-positive rate, false-negative rate, accuracy, feature work area under the curve (AUC) of subjects, the best performance of the model was chosen.Results The efficiency of the Xgboost model based on the above evaluation indicators was the highest, which the diagnosis of PMN of the sensitivity and specificity, respectively 92% and 96%.Conclusions The differential diagnosis model for PMN was established successfully and the efficiency performance of the Xgboost model was the best. It could be used for the clinical diagnosis of PMN. |
| format | Article |
| id | doaj-art-78b3b26d74fa4d76a57222f224bb84e4 |
| institution | DOAJ |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-78b3b26d74fa4d76a57222f224bb84e42025-08-20T03:22:16ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146210.1080/0886022X.2024.2380752Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithmsShangmei Cao0Shaozhe Yang1Bolin Chen2Xixia Chen3Xiuhong Fu4Shuifu Tang5Department of Science and Technology Innovation Center, Luohe Central Hospital, The First Affiliated Hospital of Luohe Medical College, Henan Key Laboratory of Fertility Protection and Aristogenesis, Luohe, ChinaDepartment of Science and Technology Innovation Center, Luohe Central Hospital, The First Affiliated Hospital of Luohe Medical College, Henan Key Laboratory of Fertility Protection and Aristogenesis, Luohe, ChinaDepartment of Science and Technology Innovation Center, Luohe Central Hospital, The First Affiliated Hospital of Luohe Medical College, Henan Key Laboratory of Fertility Protection and Aristogenesis, Luohe, ChinaDivision of Nephrology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, ChinaDepartment of Science and Technology Innovation Center, Luohe Central Hospital, The First Affiliated Hospital of Luohe Medical College, Henan Key Laboratory of Fertility Protection and Aristogenesis, Luohe, ChinaDivision of Nephrology, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, ChinaContext Four algorithms with relatively balanced complexity and accuracy in deep learning classification algorithm were selected for differential diagnosis of primary membranous nephropathy (PMN).Objective This study explored the most suitable classification algorithm for PMN identification, and to provide data reference for PMN diagnosis research.Methods A total of 500 patients were referred to Luo-he Central Hospital from 2019 to 2021. All patients were diagnosed with primary glomerular disease confirmed by renal biopsy, contained 322 cases of PMN, the 178 cases of non-PMN. Using the decision tree, random forest, support vector machine, and extreme gradient boosting (Xgboost) to establish a differential diagnosis model for PMN and non-PMN. Based on the true positive rate, true negative rate, false-positive rate, false-negative rate, accuracy, feature work area under the curve (AUC) of subjects, the best performance of the model was chosen.Results The efficiency of the Xgboost model based on the above evaluation indicators was the highest, which the diagnosis of PMN of the sensitivity and specificity, respectively 92% and 96%.Conclusions The differential diagnosis model for PMN was established successfully and the efficiency performance of the Xgboost model was the best. It could be used for the clinical diagnosis of PMN.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2380752Primary membranous nephropathymachine learning algorithmsdifferential diagnosisanti-PLA2RIgGXgboost model |
| spellingShingle | Shangmei Cao Shaozhe Yang Bolin Chen Xixia Chen Xiuhong Fu Shuifu Tang Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms Renal Failure Primary membranous nephropathy machine learning algorithms differential diagnosis anti-PLA2R IgG Xgboost model |
| title | Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms |
| title_full | Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms |
| title_fullStr | Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms |
| title_full_unstemmed | Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms |
| title_short | Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms |
| title_sort | establishing a differential diagnosis model between primary membranous nephropathy and non primary membranous nephropathy by machine learning algorithms |
| topic | Primary membranous nephropathy machine learning algorithms differential diagnosis anti-PLA2R IgG Xgboost model |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2380752 |
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