ArticleDiagnostics (Basel, Switzerland)2019
Detection of Lower Albuminuria Levels and Early Development of Diabetic Kidney Disease Using an Artificial Intelligence-Based Rule Extraction Approach.
Article in Diagnostics (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Increased tumor necrosis factor-receptor superfamily plasma levels are associated with early renal or retinal involvement in intermediate hyperglycemia.World journal of diabetes · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.Journal of diabetes science and technology · 2025Review
- The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients.BMC medical informatics and decision making · 2023Article
- Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2022Article
- Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease.Journal of healthcare engineering · 2022Article
- Chronic Kidney Disease Testing Among Primary Care Patients With Type 2 Diabetes Across 24 U.S. Health Care Organizations.Diabetes care · 2021Article
- Poor Glycemic Control Can Increase the Plasma Kidney Injury Molecule-1 Concentration in Normoalbuminuric Children and Adolescents with Diabetes Mellitus.Children (Basel, Switzerland) · 2021Article
- The evolution and future of diabetic kidney disease research: a bibliometric analysis.BMC nephrology · 2021Article
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Abstract
The aim of the present study was to determine the lowest cut-off value for albuminuria levels, which can be used to detect diabetic kidney disease (DKD) using the urinary albumin-to-creatinine ratio (UACR). National Health and Nutrition Examination Survey (NHANES) data for US adults were used, and participants were classified as having diabetes or prediabetes based on a self-report and physiological measures. The study dataset comprised 942 diabetes and 524 prediabetes samples. This study clarified the significance of the lower albuminuria (UACR) levels, which can detect DKD, using an artificial intelligence-based rule extraction approach. The diagnostic rules (15 concrete rules) for both samples were extracted using a recursive-rule eXtraction (Re-RX) algorithm with continuous attributes (continuous Re-RX) to discriminate between prediabetes and diabetes datasets. Continuous Re-RX showed high test accuracy (77.56%) and a large area under the receiver operating characteristics curve (75%), which derived the two cut-off values (6.1 mg/g Cr and 71.00 mg/g Cr) for the lower albuminuria level in the UACR to detect early development of DKD. The early cut-off values for normoalbuminuria (NA) and microalbuminuria (MA) will be determined to help detect CKD and DKD, and to detect diabetes before MA develop and to prevent diabetic complications.
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