Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults

Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD). We utilized longitudinal data from 1365 Chinese, Malay, and Indian particip...

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Vydáno v:	eLife Ročník 12
Hlavní autoři:	Sabanayagam, Charumathi, He, Feng, Nusinovici, Simon, Li, Jialiang, Lim, Cynthia, Tan, Gavin, Cheng, Ching Yu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England eLife Sciences Publications Ltd 14.09.2023 eLife Sciences Publications, Ltd
Témata:	Adult Antidiabetics Biomarkers Blood pressure Body mass index Cholesterol Cohort analysis Cohort Studies Creatinine Data collection Diabetes Diabetes Mellitus Diabetic Nephropathies - diagnosis Diabetic Nephropathies - epidemiology Diabetic nephropathy Diabetic retinopathy DKD elastic net Epidemiology Epidemiology and Global Health Epidermal growth factor receptors Eye diseases Fatty acids GBDT Glomerular filtration rate Glucose Glycated Hemoglobin High density lipoprotein Humans incidence Ketones Kidney diseases Learning algorithms Lipid metabolism Lipids Lipoproteins Machine Learning Medical research Metabolism Metabolites Minority & ethnic groups Performance evaluation Population Population studies Population-based studies Predictions predictors Research Design Retinopathy Risk factors Singapore global health predictors epidemiology GBDT none DKD elastic net incidence metabolites
ISSN:	2050-084X, 2050-084X
On-line přístup:	Získat plný text
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