Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques

dc.contributor.authorRodriguez‐Romero, Violeta
dc.contributor.authorBergstrom, Richard F.
dc.contributor.authorDecker, Brian S.
dc.contributor.authorLahu, Gezim
dc.contributor.authorVakilynejad, Majid
dc.contributor.authorBies, Robert R.
dc.contributor.departmentMedicine, School of Medicineen_US
dc.date.accessioned2019-12-26T22:08:57Z
dc.date.available2019-12-26T22:08:57Z
dc.date.issued2019-09
dc.description.abstractApplying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to identify such factors using ML. The longitudinal data were stratified by time after patient enrollment to differentiate early and late predictors. Our results showed that Random Forest and Simple Logistic Regression methods exhibited the best performance among the evaluated algorithms. Baseline values for glomerular filtration rate (GFR), urinary creatinine, urinary albumin, potassium, cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio were identified as DN predictors. Early predictors were the baseline values of GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and potassium at month 4. Changes per year in GFR, FPG, and triglycerides were recognized as predictors of late development. In conclusion, ML-based methods successfully identified predictive factors for DN among patients with T2DM.en_US
dc.identifier.citationRodriguez-Romero, V., Bergstrom, R. F., Decker, B. S., Lahu, G., Vakilynejad, M., & Bies, R. R. (2019). Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques. Clinical and translational science, 12(5), 519–528. doi:10.1111/cts.12647en_US
dc.identifier.urihttps://hdl.handle.net/1805/21592
dc.language.isoen_USen_US
dc.publisherWileyen_US
dc.relation.isversionof10.1111/cts.12647en_US
dc.relation.journalClinical and Translational scienceen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourcePMCen_US
dc.subjectData miningen_US
dc.subjectMachine learningen_US
dc.subjectBiomarkersen_US
dc.subjectDiabetic nephropathyen_US
dc.titlePrediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniquesen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
CTS-12-519.pdf
Size:
376.79 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.99 KB
Format:
Item-specific license agreed upon to submission
Description: