ArticleIET systems biology2021
A two-stage neural network prediction of chronic kidney disease.
Article in IET systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Applications of artificial intelligence in chronic kidney disease: a systematic review.Revista de saude publica · 2026Pooled it
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage.PeerJ. Computer science · 2025Article
- Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate.Scientific reports · 2024Article
- AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease.Scientific reports · 2024Article
- Three-dimensional convolutional neural network-based classification of chronic kidney disease severity using kidney MRI.Scientific reports · 2024Article
- A Metabolomics study of metabolites associated with the glomerular filtration rate.BMC nephrology · 2023Article
- SRAS-net: Low-resolution chromosome image classification based on deep learning.IET systems biology · 2022Article
- Identification of Metabolite Markers Associated with Kidney Function.Journal of immunology research · 2022Article
- A two-stage neural network prediction of chronic kidney disease.IET systems biology · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usually approximated by the estimated glomerular filtration rate, underscoring an urgent need for more precise and stable approaches. With the introduction of novel machine learning methodologies, prediction performance is shown to be significantly improved across all available data, but the performance is still limited because of the lack of models in dealing with ultra-high dimensional datasets. This study aims to provide a two-stage neural network approach for prediction of GFR and to suggest some other useful biomarkers obtained from the blood metabolites in measuring GFR. It is a composite of feature shrinkage and neural network when the number of features is much larger than the number of training samples. The results show that the proposed method outperforms the existing ones, such as convolutionneural network and direct deep neural network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.