[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120005-en":3,"doc-seo-120005-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120005,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit - Article","Multiple organ failures drive the highest mortality and morbidity burden in the ICU, where clinical progression is commonly tracked through the Sequential Organ Failure Assessment (SOFA) score. This study classifies multiple organ failures using machine learning algorithms trained on retrospective data from 98 ICU patients from Universiti Malaya Medical Centre. Models include decision tree, linear discriminant, naïve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest, evaluated via 10-fold cross-validation with 80/20 splits. Random forest achieved 99.8% accuracy and 99.9% sensitivity in training, while AdaBoost reached 99.1% sensitivity in testing. Feature analysis identified respiratory rate and mean arterial pressure as key variables by chi-square testing, and insulin plus fraction of oxygenated hemoglobin as top predictors via mutual information.","Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit  \nNorliyana Nor Hisham Shah1, Normy Norfiza Abdul Razak1*, Athirah Abdul Razak1, Asma’ Abu-Samah2, Fatanah M. Suhaimi3, Ummu Jamaluddin4  \n1 Institute of Energy Infrastructure (IEI), College of Engineering,  \nUniversiti Tenaga Nasional, Kajang, Selangor, 43000, MALAYSIA  \n2 Faculty of Engineering and Built Environment,  \nUniversiti Kebangsaan Malaysia, Bangi, Selangor, 43600, MALAYSIA  \n3 Advanced Medical and Dental Institute,  \nUniversiti Sains Malaysia, Kepala Batas, Penang, 13200, MALAYSIA  \n4 College of Engineering,  \nUniversiti Malaysia Pahang, Pekan, Pahang, 26000, MALAYSIA  \n*[Corresponding Author: Normy@uniten.edu.my](Corresponding Author: Normy@uniten.edu.my)[ ](Corresponding Author: Normy@uniten.edu.my)DOI: [https://doi.org/10.30880/ijie.2024.16.02.012](https://doi.org/10.30880/ijie.2024.16.02.012)  \nArticle Info  \nReceived: 19 November 2023  \nAccepted: 2 January 2024  \nAvailable online: 29 April 2024  \nKeywords  \nMultiple organ failures, machine learning, classifications, intensive care unit  \nAbstract  \nMultiple 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, naïve 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 crossvalidations 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 featureselection shows respiratory rate and mean arterial pressure (MAP) asthe most important variables using chi-square test while insulin and fraction of oxygenated hemoglobin are the most important predictors by the mutual information test.  \n1. Introduction  \nMultiple organ failures (MOF) are defined as the presence of two or more organ dysfunctions simultaneously. The term is sometimes interchangeably used with multiple organ dysfunction syndrome to describe improving organ function after receiving treatment. A more known representation of organ failure in the ICU are such as acute respiratory distress syndrome (ARDS), disseminated intravascular coagulation (DIC), or acute kidney injury (AKI) . Sepsis is the main cause of organ failure as a response to infection, and septic shock is usually described for patients with multiple organ failures [1] . Other causes such as burn, trauma, and hematologic malignancies  \npatients also developed multiple organ failures [2-5] . In critically ill patients with COVID-19, MOFs are the main cause of mortality [6] .  \nSeveral risk scores were developed to assess organ failure and mortality among patients in the ICU. The most used score for organ failure is the Sequential Organ Failure Assessment (SOFA) score [7]. Another known severity score for organ failure is the multiple organ dysfunction score (MODS) [8] . Both these scores evaluate the same organ systems which are respiratory, cardiovascular, renal, hepatic, coagulation, and central nervous system. These severity scores are preferred as they use a single variable to monitor each organ failure progression. In SOFA score, each organ is given a score between 0 to 4, where a score of 4 indicates severe o","cbCaielsbiw3M6Dk","https://ap.wps.com/l/cbCaielsbiw3M6Dk","pdf",495901,1,9,"English","en",105,"# Introduction\n## Definition and causes of multiple organ failures\n## ICU risk scores and SOFA score framework\n## Machine learning in MOF, sepsis, and mortality prediction","[{\"question\":\"What clinical measure does the study use to model multiple organ failures?\",\"answer\":\"The classification is based on the Sequential Organ Failure Assessment (SOFA) score, monitored daily using the worst readings for each organ.\"},{\"question\":\"Which machine learning algorithms were evaluated for MOF classification?\",\"answer\":\"Decision tree, linear discriminant, naïve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for classification.\"},{\"question\":\"How were the models trained and tested?\",\"answer\":\"Data from 98 ICU patients were used retrospectively; classifiers were trained on 80% of patients and assessed on the remaining 20% using 10-fold cross-validation with 34 ICU variables.\"}]","Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit - Article | PDF",1785727662,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-classifications-of-multiple-organ-failures-in-a-malaysian-intensive-care-unit-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-classifications-of-multiple-organ-failures-in-a-malaysian-intensive-care-unit-article/120005/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical measure does the study use to model multiple organ failures?","Question",{"text":75,"@type":76},"The classification is based on the Sequential Organ Failure Assessment (SOFA) score, monitored daily using the worst readings for each organ.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were evaluated for MOF classification?",{"text":80,"@type":76},"Decision tree, linear discriminant, naïve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the models trained and tested?",{"text":84,"@type":76},"Data from 98 ICU patients were used retrospectively; 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