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Review of Acute Kidney Injury Classification Using Machine Learning

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dc.contributor.authorShah N.N.H.en_US
dc.contributor.authorRazak N.en_US
dc.contributor.authorAbu-Samah A.en_US
dc.contributor.authorRazak A.A.en_US
dc.contributor.authorid7401823793en_US
dc.contributor.authorid37059587300en_US
dc.contributor.authorid56719596600en_US
dc.contributor.authorid56960052400en_US
dc.date.accessioned2023-05-29T09:08:51Z
dc.date.available2023-05-29T09:08:51Z
dc.date.issued2021
dc.descriptionAdaptive 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 systemsen_US
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9398774
dc.identifier.doi10.1109/IECBES48179.2021.9398774
dc.identifier.epage328
dc.identifier.scopus2-s2.0-85104875917
dc.identifier.spage324
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85104875917&doi=10.1109%2fIECBES48179.2021.9398774&partnerID=40&md5=2c81b6a9ab8db6dfdacd7064744f9fee
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26300
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020
dc.titleReview of Acute Kidney Injury Classification Using Machine Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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