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http://ir.nhri.org.tw/handle/3990099045/14905
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| Title: | Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms |
| Authors: | Ou, SM;Tsai, MT;Lee, KH;Tseng, WC;Yang, CY;Chen, TH;Bin, PJ;Chen, TJ;Lin, YP;Sheu, WHH;Chu, YC;Tarng, DC |
| Contributors: | Institute of Molecular and Genomic Medicine |
| Abstract: | ObjectivesType 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings.MethodsWe established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with 70% and 30% of patients randomly assigned to the training and testing sets, respectively.ResultsThe discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender.ConclusionsBecause our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage. |
| Date: | 2023-03-10 |
| Relation: | BioData Mining. 2023 Mar 10;16:Article number 8. |
| Link to: | http://dx.doi.org/10.1186/s13040-023-00324-2 |
| JIF/Ranking 2023: | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1756-0381&DestApp=IC2JCR |
| Cited Times(WOS): | https://www.webofscience.com/wos/woscc/full-record/WOS:000947755100001 |
| Cited Times(Scopus): | https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85150181757 |
| Appears in Collections: | [許惠恒] 期刊論文
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| ISI000947755100001.pdf | | 1674Kb | Adobe PDF | 102 | View/Open |
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