[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126080-en":3,"doc-seo-126080-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126080,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Mortality prediction of COVID-19 patients using supervised machine learning","Hospitalized patients with COVID-19 face an elevated risk of mortality, motivating the development of supervised machine learning models for accurate outcome prediction. This study evaluates multiple ML algorithms using demographic, clinical, and laboratory predictors available at registration. Data from 4,314 eligible patients from three hospitals in Yogyakarta, Indonesia (3,384 survivors and 930 deaths) were analyzed and model performance assessed with a confusion matrix. Random forest achieved the best overall results (AUC 90.02%) and identified key predictors including myocardial infarction, SpO2, neutrophil, D-dimer, and creatinine.","Mortality prediction of COVID-19 patients using supervised  \nmachine learning  \nHusnul Khuluq1, Prasandhya Astagiri Yusuf2, Dyah Aryani Perwitasari3, Thang Nguyen4  \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,  \nJakarta, Indonesia  \n3Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta, Indonesia  \n4Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, University of Medicine and Pharmacy,  \nCan Tho, Vietnam  \nArticle history:  \nReceived Jul 31, 2023 Revised Nov 6, 2023 Accepted Dec 12, 2023  \nKeywords:  \nAttribute evaluation Confusion matrix  \nData balancing Synthetic minority oversampling technique Ten-fold cross-validation  \nCorresponding Author:  \nHospitalized patients with COVID-19 are at higher risk of mortality. Machine learning (ML) algorithms have been proposed as a possible strategy for predicting mortality rates among patients hospitalized with COVID-19. This study analyzed various ML algorithms and identified the best model to predict COVID-19 mortality based on demographic, clinical, and laboratory data collected at registration. Data from 4,314 eligible patients (3,384 survivorsand 930 who died) was collected from the register of three hospitals in Yogyakarta province, Indonesia, based on the confirmed predictors. Next, ML algorithms were utilized to predict mortality. Finally, the confusion matrix was used to evaluate how effective the models performed. The best five predictors from 26 features were myocardial infarction, SpO2, neutrophil, Ddimer, and creatinine. The results indicate that the random forest algorithm showed better performance than other ML algorithms in terms of accuracy, sensitivity, precision, specificity, and area under the curve (AUC), achieving values of 84.15%, 84.0%, 84.1%, 83.9%, and 90.02%, respectively. Implementing ML techniques can accurately predict the mortality rate associated with COVID-19. Therefore, this predictive model can help clinicians and hospitals predict COVID patients with a greater risk of death and effectively target more appropriate treatments.  \nThis is an open access article under the CC BY-SA license.  \nHusnul Khuluq  \nDepartment of Pharmacy, Faculty of Health Sciences, Universitas Muhammadiyah Gombong St. Yos Sudarso 461, Gombong, Kebumen, Indonesia  \n[Email: husnulkhuluq@unimugo.ac.id](Email: husnulkhuluq@unimugo.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe new COVID-19 was first identified in Wuhan District, Republic of China, in December 2019 [1] . Since then, this infectious disease has rapidly spread worldwide. The World Health Organization (WHO) established the outbreak as a pandemic in January 2020 [2] . The COVID-19 virus showed various clinical outcomes, ranging from asymptomatic or mild symptoms to severe complications and, in specific cases, fatalities. The virus is highly transmissible and has rapidly spread worldwide, becoming a significant global health threat. The rapid transmission of COVID-19 caused a significant failure of medical resources and a decrease in frontline medical personnel [3]–[5] .  \nMany COVID-19 patients rapidly get worse after having had initially mild symptoms. This demonstrates the importance of improved risk-strategy models. By using predictive models, clinicians may determine whether patients are more likely to die, targeting urgent help to them to ensure fewer individuals die  \n[5]–[7] . Therefore, to reduce the impact on the medical system and provide patients with the most effective care possible, it is crucial to exactly predict the prognosis of the disease and prioritize the treatment of patients who are in critical condition. Clinicians and health authorities have relied on computing and statistical model projections because of the unpredictable nature of their effects. [8], [9] . In solution to the abo","cbCaiev5338W9nYH","https://ap.wps.com/l/cbCaiev5338W9nYH","pdf",481463,7,1,"English","en",105,"# Introduction\n## Machine learning for clinical risk prediction\n# Method\n## Study population and inclusion criteria\n## Data sources and predictors\n## Model training and evaluation","[{\"question\":\"What is the study’s main goal for COVID-19 patients?\",\"answer\":\"To build a supervised machine learning model that predicts mortality risk for hospitalized COVID-19 patients using demographic, clinical, and laboratory data at registration.\"},{\"question\":\"How were patients selected and how many were included?\",\"answer\":\"A retrospective cohort included 4,314 confirmed COVID-19 patients from three hospitals in Yogyakarta, Indonesia, hospitalized between January 2020 and December 2022, excluding records with missing/incomplete data and other specified exclusions.\"},{\"question\":\"Which algorithm performed best and which predictors mattered most?\",\"answer\":\"Random forest outperformed other ML models in accuracy, sensitivity, precision, specificity, and AUC (90.02%). The top predictors from 26 features included myocardial infarction, SpO2, neutrophil, D-dimer, and creatinine.\"}]","Mortality prediction of COVID-19 patients using supervised machine learning | PDF",1785902968,20,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"mortality-prediction-of-covid-19-patients-using-supervised-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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/mortality-prediction-of-covid-19-patients-using-supervised-machine-learning/126080/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the study’s main goal for COVID-19 patients?","Question",{"text":76,"@type":77},"To build a supervised machine learning model that predicts mortality risk for hospitalized COVID-19 patients using demographic, clinical, and laboratory data at registration.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were patients selected and how many were included?",{"text":81,"@type":77},"A retrospective cohort included 4,314 confirmed COVID-19 patients from three hospitals in Yogyakarta, Indonesia, hospitalized between January 2020 and December 2022, excluding records with missing/incomplete data and other specified exclusions.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithm performed best and which predictors mattered most?",{"text":85,"@type":77},"Random forest outperformed other ML models in accuracy, sensitivity, precision, specificity, and AUC (90.02%). 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