[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118908-en":3,"doc-seo-118908-105":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},118908,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Effectiveness of Machine Learning for COVID-19 Patient Mortality Prediction using WEKA","Timely detection of COVID-19 inpatients at high risk of mortality supports better triage, bed allocation, and faster clinical decisions. This research aims to build and validate individualized mortality risk assessments using anonymous demographic, clinical, and laboratory findings recorded at admission, and to evaluate death prediction with machine learning. Data from 2,313 patients collected between January 2020 and July 2022 in two hospitals were analyzed using AdaBoost, logistic regression, random forest, support vector machine, naïve Bayes, and decision tree in WEKA 3.8.6. Random forest achieved the highest overall performance, and key predictors included septic shock, respiratory failure, and D-dimer.","Global Medical and Health Communication  \nOnline submission: [https://ejournal.unisba.ac.id/index.php/gmhc](https://ejournal.unisba.ac.id/index.php/gmhc)  \nDOI: [https://doi.org/10.29313/gmhc.v11i3.12119](https://doi.org/10.29313/gmhc.v11i3.12119)  \nGMHC. 2023;11(3):200–208 pISSN 2301-9123 │ eISSN 2460-5441  \nRESEARCH ARTICLE  \nEffectiveness of Machine Learning for COVID-19 Patient Mortality  \nPrediction using WEKA  \nHusnul Khuluq,1 PrasandhyaAstagiri Yusuf,2 Dyah Aryani Perwitasari3  \n1Department of Pharmacy, Faculty of Health Sciences, Universitas Muhammadiyah Gombong, Kebumen, Indonesia, 2Department of Medical Physiology and Biophysics/Medical Technology Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Central Jakarta, Indonesia, 3Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta, Indonesia  \nAbstract  \nTimely detection of patients with a high mortality risk in coronavirus disease 2019 (COVID-19) can substantially improve triage, bed allocation, time reduction, and potential outcomes. A potential solution is using machine learning (ML) algorithms to predict mortality in COVID-19 hospitalized patients. The study's objective was to create and verify individual risk assessments for mortality using anonymous demographic, clinical, and laboratory findings at admission, as well as to assess the possibility of death using machine learning. We used a standardized format and electronic medical records. Data from 2,313 patients were collected from two Muhammadiyah hospitals from January 2020 to July 2022. Utilizing each patient's clinical manifestation state at admission and laboratory parameters, 24 demographic, clinical, and laboratory results were studied. The algorithms analyzed were AdaBoost, logistic regression, random forest, support vector machine, naïve Bayes, and decision tree, which were applied through WEKA version 3.8.6. Random forest performed better than the other machine learning techniques, with precision, sensitivity, receiver operating characteristic (ROC), and accuracy of 78.6%, 78.7%, 85%, and 78.65%, respectively. The three top predictors were septic shock (OR=21.518, 95% CI=4.933–93.853), respiratory failure (OR=15.503, 95% CI=8.507–28.254), and D-dimer (OR=3.288, 95% CI=2.510–4.306) . Machine learning–based predictive models, especially the random forest algorithm, may make it easier to identify patients at high risk of death and guide physicians' appropriate interventions.  \nKeywords: Data mining, inpatient mortality, machine learning algorithm, prediction model  \nIntroduction  \nIn 2019, Wuhan province in China identified the first case of a novel coronavirus, which is considered to have been transferred from animals to humans.1 The virus is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . Originally known as the 2019 novel coronavirus (2019-nCoV), the disease is now known as COVID-19 .2 In March 2020, the World Health Organization declared COVID-19 a pandemic.3  \nWhen COVID-19 patients are first admitted, doctors frequently can only adequately determine their prognosis once the disease has worsened. Additionally, the course ofCOVID-19 can change abruptly, causing a patient with a stable status to develop a critical condition quickly.4 Elderly age, male, and the presence of various comorbidities, such as diabetes, high blood pressure, high cholesterol levels, cardiovascular disease, and chronic kidney disease, have been linked to increased mortality rates and severe outcomes in individuals affected by COVID-19 .5,6  \nArtificial intelligence (AI) is a discipline in computer science that aims to comprehend and construct intelligent entities, typically manifested as software programs.7 AI research has utilized machine learning techniques, which can consider intricate relationships to detect patterns within the given data. Standard machine learning algorithms can be broadly categorized into two sorts based on the tasks they aim to solve: supervised and unsupervised.7  \nThe study ab","cbCairhWztgQyn5Y","https://ap.wps.com/l/cbCairhWztgQyn5Y","pdf",558956,1,9,"English","en",105,"# Abstract\n# Introduction\n# Methods","[{\"question\":\"What problem does the study address for COVID-19 patients?\",\"answer\":\"It addresses the challenge that clinicians often determine prognosis only after the disease worsens, and that patient conditions can deteriorate rapidly after admission.\"},{\"question\":\"Which machine learning algorithms were evaluated in the WEKA-based analysis?\",\"answer\":\"AdaBoost, logistic regression, random forest, support vector machine, naïve Bayes, and decision tree were tested using WEKA version 3.8.6.\"},{\"question\":\"Which algorithm performed best and what major predictors were identified?\",\"answer\":\"Random forest showed the best performance. The top predictors were septic shock, respiratory failure, and elevated D-dimer levels.\"}]","Effectiveness of Machine Learning for COVID-19 Patient Mortality Prediction using WEKA | PDF",1785720907,23,{"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},"effectiveness-of-machine-learning-for-covid-19-patient-mortality-prediction-using-weka","",{"@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/effectiveness-of-machine-learning-for-covid-19-patient-mortality-prediction-using-weka/118908/",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 problem does the study address for COVID-19 patients?","Question",{"text":75,"@type":76},"It addresses the challenge that clinicians often determine prognosis only after the disease worsens, and that patient conditions can deteriorate rapidly after admission.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were evaluated in the WEKA-based analysis?",{"text":80,"@type":76},"AdaBoost, logistic regression, random forest, support vector machine, naïve Bayes, and decision tree were tested using WEKA version 3.8.6.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performed best and what major predictors were identified?",{"text":84,"@type":76},"Random forest showed the best performance. 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