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Machine learning models and statistical measures for predicting the progression of IgA nephropathy
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Junhyug | - |
dc.contributor.author | Punithan, Dharani | - |
dc.contributor.author | Lee, Hajeong | - |
dc.contributor.author | Lee, JungPyo | - |
dc.contributor.author | Kim, YonSu | - |
dc.contributor.author | Kim, DongKi | - |
dc.contributor.author | McKay, Ri (Bob) | - |
dc.date.accessioned | 2024-08-08T01:40:07Z | - |
dc.date.available | 2024-08-08T01:40:07Z | - |
dc.date.created | 2018-10-23 | - |
dc.date.created | 2018-10-23 | - |
dc.date.issued | 2015-06 | - |
dc.identifier.citation | International Journal of Software Engineering and Knowledge Engineering, Vol.25 No.5, pp.829-849 | - |
dc.identifier.issn | 0218-1940 | - |
dc.identifier.uri | https://hdl.handle.net/10371/207195 | - |
dc.description.abstract | We predict the progression of Immunoglobulin A Nephropathy using three classification methods: Classification and Regression Trees, Logistic Regression, and Feed-Forward Artificial Neural Networks. We treat it as a classification problem, of predicting progression to end-stage renal disease in the ten years following initial diagnosis. We compared classifier performance using ROC analysis. All three methods yielded good classifiers, with AUC between 0.85 and 0.95. The results were generally in-line with expectations, with poor kidney performance on presentation, and evident macroscopic and microscopic damage, all associated with poorer prognosis. | - |
dc.language | 영어 | - |
dc.publisher | World Scientific Publishing Co | - |
dc.title | Machine learning models and statistical measures for predicting the progression of IgA nephropathy | - |
dc.type | Article | - |
dc.identifier.doi | 10.1142/S0218194015400227 | - |
dc.citation.journaltitle | International Journal of Software Engineering and Knowledge Engineering | - |
dc.identifier.wosid | 000361952700003 | - |
dc.identifier.scopusid | 2-s2.0-84942581596 | - |
dc.citation.endpage | 849 | - |
dc.citation.number | 5 | - |
dc.citation.startpage | 829 | - |
dc.citation.volume | 25 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Lee, JungPyo | - |
dc.contributor.affiliatedAuthor | Kim, YonSu | - |
dc.contributor.affiliatedAuthor | Kim, DongKi | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | GLOMERULONEPHRITIS | - |
dc.subject.keywordPlus | WORLDWIDE | - |
dc.subject.keywordPlus | DISEASE | - |
dc.subject.keywordAuthor | Immunoglobulin A Nephropathy (IgAN) | - |
dc.subject.keywordAuthor | End-Stage Renal Disease (ESRD) | - |
dc.subject.keywordAuthor | Classification and Regression Trees (CART) | - |
dc.subject.keywordAuthor | Logistic Regression | - |
dc.subject.keywordAuthor | Neural Networks | - |
dc.subject.keywordAuthor | Receiver Operating Characteristic (ROC) | - |
dc.subject.keywordAuthor | Area Under Curve (AUC) | - |
dc.subject.keywordAuthor | Closest-Top-Left (CTL) | - |
dc.subject.keywordAuthor | Youden&apos | - |
dc.subject.keywordAuthor | s index | - |
dc.subject.keywordAuthor | Missing Completely At Random (MCAR) | - |
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