Publication:
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit

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dc.citedby0
dc.contributor.authorShah N.N.H.en_US
dc.contributor.authorRazak N.N.A.en_US
dc.contributor.authorRazak A.A.en_US
dc.contributor.authorAbu-Samah A.en_US
dc.contributor.authorSuhaimi F.M.en_US
dc.contributor.authorJamaluddin U.en_US
dc.contributor.authorid7401823793en_US
dc.contributor.authorid37059587300en_US
dc.contributor.authorid56960052400en_US
dc.contributor.authorid56719596600en_US
dc.contributor.authorid36247893200en_US
dc.contributor.authorid55330889600en_US
dc.date.accessioned2025-03-03T07:46:52Z
dc.date.available2025-03-03T07:46:52Z
dc.date.issued2024
dc.description.abstractMultiple organ failures are the main cause of mortality and morbidity in the intensive care unit (ICU). The progression of organ failures in the ICU is usually monitored using the Sequential Organ Failure Assessment (SOFA) score. This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. Ninety-eight ICU patients? data were obtained retrospectively from Universiti Malaya Medical Centre for analysis. Several machine learning algorithms which are decision tree, linear discriminant, na�ve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. The classifiers were trained on 80% of the patients with 10-fold cross-validations and assessed on 20% of patients using 34 variables in the ICU. The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. Meanwhile, the AdaBoost algorithm achieved 99.1% sensitivity in the testing dataset. This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. The feature selection shows respiratory rate and mean arterial pressure (MAP) as the most important variables using chi-square test while insulin and fraction of oxygenated hemoglobin are the most important predictors by the mutual information test. ? This is an open access article under the CC BY-NC-SA 4.0 licenseen_US
dc.description.natureFinalen_US
dc.identifier.doi10.30880/ijie.2024.16.02.012
dc.identifier.epage122
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85195254601
dc.identifier.spage114
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85195254601&doi=10.30880%2fijie.2024.16.02.012&partnerID=40&md5=df916c6e34c35ef679e8c8da88675cad
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37040
dc.identifier.volume16
dc.pagecount8
dc.publisherPenerbit UTHMen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Integrated Engineering
dc.titleMachine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Uniten_US
dc.typeArticleen_US
dspace.entity.typePublication
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