Open AccessA two‐stage neural network prediction of chronic kidney disease
Author(s) -
Peng Hongquan,
Zhu Haibin,
Ieong Chi Wa Ao,
Tao Tao,
Tsai Tsung Yang,
Liu Zhi
Publication year - 2021
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/syb2.12031
Subject(s) - artificial neural network , benchmark (surveying) , computer science , kidney disease , renal function , artificial intelligence , machine learning , stage (stratigraphy) , feature (linguistics) , data mining , filtration (mathematics) , pattern recognition (psychology) , medicine , statistics , mathematics , biology , paleontology , linguistics , philosophy , geodesy , geography
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.