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Review of Acute Kidney Injury Classification Using Machine Learning

Adaptive boosting; Biomedical engineering; Decision trees; Forecasting; Intensive care units; Nearest neighbor search; Sensitivity analysis; Support vector machines; Complementary data; Gradient boosting; Insulin sensitivity; K-nearest neighbours; Kidney function; Machine learning techniques; Perfor...

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Main Authors: Shah N.N.H., Razak N., Abu-Samah A., Razak A.A.
Other Authors: 7401823793
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-263002023-05-29T17:08:51Z Review of Acute Kidney Injury Classification Using Machine Learning Shah N.N.H. Razak N. Abu-Samah A. Razak A.A. 7401823793 37059587300 56719596600 56960052400 Adaptive boosting; Biomedical engineering; Decision trees; Forecasting; Intensive care units; Nearest neighbor search; Sensitivity analysis; Support vector machines; Complementary data; Gradient boosting; Insulin sensitivity; K-nearest neighbours; Kidney function; Machine learning techniques; Performance based; Receiver operating characteristic curves; Learning systems The incidence of acute kidney injury (AKI) across hospitalized patients, especially in the intensive care unit (ICU) is worrying due to its prevalence and association with mortality. The sudden decrease in kidney function can be identified by an increase in serum creatinine or decreasing urine output. The severity of AKI stages can be defined according to Kidney Disease: Improving Global Outcomes (KDIGO) classifications. Several studies have reported AKI associated risk factors such as sepsis and rates of mortality. Due to this concern, machine learning has been implemented to predict AKI incidences utilizing several techniques such as Decision Tree, Random Forest, Support Vector Machine, k-Nearest Neighbour, and Gradient Boosting Method. The performances of these models were measured by area under the receiver operating characteristic curve (AUROC). This review examines ICU-based AKI incidences and the use of machine learning techniques to predict AKI incidences. It highlights the complementary data used to perform the prediction and its performance based on AUROC. The models studied in this review demonstrated AUROCs between 0.57 to 0.95. Diabetes and hyperglycemia have been demonstrated as significant risk factors for AKI in the ICU. Hence, insulin sensitivity representing a patient's metabolic variation is suggested as another variable to predict AKI incidence. � 2021 IEEE. Final 2023-05-29T09:08:51Z 2023-05-29T09:08:51Z 2021 Conference Paper 10.1109/IECBES48179.2021.9398774 2-s2.0-85104875917 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104875917&doi=10.1109%2fIECBES48179.2021.9398774&partnerID=40&md5=2c81b6a9ab8db6dfdacd7064744f9fee https://irepository.uniten.edu.my/handle/123456789/26300 9398774 324 328 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Adaptive boosting; Biomedical engineering; Decision trees; Forecasting; Intensive care units; Nearest neighbor search; Sensitivity analysis; Support vector machines; Complementary data; Gradient boosting; Insulin sensitivity; K-nearest neighbours; Kidney function; Machine learning techniques; Performance based; Receiver operating characteristic curves; Learning systems
author2 7401823793
author_facet 7401823793
Shah N.N.H.
Razak N.
Abu-Samah A.
Razak A.A.
format Conference Paper
author Shah N.N.H.
Razak N.
Abu-Samah A.
Razak A.A.
spellingShingle Shah N.N.H.
Razak N.
Abu-Samah A.
Razak A.A.
Review of Acute Kidney Injury Classification Using Machine Learning
author_sort Shah N.N.H.
title Review of Acute Kidney Injury Classification Using Machine Learning
title_short Review of Acute Kidney Injury Classification Using Machine Learning
title_full Review of Acute Kidney Injury Classification Using Machine Learning
title_fullStr Review of Acute Kidney Injury Classification Using Machine Learning
title_full_unstemmed Review of Acute Kidney Injury Classification Using Machine Learning
title_sort review of acute kidney injury classification using machine learning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806428054988259328
score 13.223943

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