Abstract
Applying 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.
| Original language | English (US) |
|---|---|
| Journal | Clinical and translational science |
| DOIs | |
| State | Published - Jan 1 2019 |
Fingerprint
ASJC Scopus subject areas
- Neuroscience(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Pharmacology, Toxicology and Pharmaceutics(all)
Cite this
Prediction of Nephropathy in Type 2 Diabetes : An Analysis of the ACCORD Trial Applying Machine Learning Techniques. / Rodriguez-Romero, Violeta; Bergstrom, Richard F.; Decker, Brian; Lahu, Gezim; Vakilynejad, Majid; Bies, Robert.
In: Clinical and translational science, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Prediction of Nephropathy in Type 2 Diabetes
T2 - An Analysis of the ACCORD Trial Applying Machine Learning Techniques
AU - Rodriguez-Romero, Violeta
AU - Bergstrom, Richard F.
AU - Decker, Brian
AU - Lahu, Gezim
AU - Vakilynejad, Majid
AU - Bies, Robert
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Applying 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.
AB - Applying 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.
UR - http://www.scopus.com/inward/record.url?scp=85066482965&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066482965&partnerID=8YFLogxK
U2 - 10.1111/cts.12647
DO - 10.1111/cts.12647
M3 - Article
JO - Clinical and Translational Science
JF - Clinical and Translational Science
SN - 1752-8054
ER -