[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125496-en":3,"doc-seo-125496-105":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},125496,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Multivariate Analysis and Machine Learning - Mortality Predictions In COVID-19 Patients from Comorbidity, Demographic and Laboratory Findings","Study objective focuses on predicting mortality risk among COVID-19 patients using machine learning models supported by multivariate analysis of clinical, demographic, and laboratory variables. A retrospective dataset of 4711 cases was assembled from public data and evaluated with six supervised algorithms (KNN, Naive Bayes, SVM, decision tree, random forest, logistic regression) under 10-fold cross-validation. Model quality was measured via precision, recall, F-measure accuracy, and ROC AUC. Logistic regression achieved the strongest ROC performance and identified MAP, stroke, age, and IL6 as key predictors.","Multivariate Analysis and Machine Learning: Mortality Predictions In COVID-19 Patients from Comorbidity, Demographic and Laboratory  \nFindings  \nHusnul Khuluq1, Prasandhya Astagiri Yusuf 2, Dyah Aryani Perwitasari 3, Abdul Fadhil 4  \n1 Dept. of Pharmacy, Faculty of Health Sciences, Universitas Muhammadiyah Gombong  \n2 Dept. of Medical Physiology of Biophysis / Medical Technology Cluster IMERI, Faculty of Medicine, Universitas Indonesia  \n3 Faculty of Pharmacy, Universitas Ahmad Dahlan  \n4 Dept. of Electrical Engineering, Technical Faculty, Universitas Ahmad Dahlan  \nReceived: 24-June-2023  \nRevised: 27-July-2023  \nAccepted: 21-August-2023  \nAbstract  \nObjective: COVID-19 Patients were constantly at a risk of death. It has been demonstrated that the utilization of machine learning (ML) algorithms could be a possible strategy for prediction mortality. Aim: This study aimed to analysis six Machine Learning (ML) algorithms in an multivariate analysis to identify key clinical, demographic and laboratory finding to predict mortality in COVID-19 pandemic Materials and methods: This retrospective study consisted of persons-under-investigation for COVID-19. Dataset taken from data science community ([kaggle.com](kaggle.com)), predictive models of mortality were constructed and compared using six supervised machine learning algorithms: KNN, naivebayes, SVM, decision tree, random forest and logistics regression using 10-fold cross-validation and multivariate analysis. The performance of algorithms was assessed using precision, recall, Fmeasure accuracy and area under the receiver operating characteristic curve (ROC) . The Waikato Environment for Knowledge Analysis (WEKA) version 3.8.6 for analysis. Multivariate analysis using Logistic regression were used to predict mortality. Results: A total of 4711 patients were included in the analysis. The top 4 mortality predictors were Mean Artery Pressure (MAP) (p\u003C0.001; OR 17.071(12.233-23.820), stroke (p\u003C0.001;OR 3.499(1.883-6.503), Age (p\u003C0.001;OR 3.23(2.716-3.830), IL6 (p\u003C0.001; OR 2.03(1.512-2.725. Logistic regression was the best ML algorithms predicted mortality with 81% ROC. Conclusion: This study identifies important independent clinical variables that predict COVID-19 infection-related mortality. The prediction method is helpful, easily improved, and easily retrained with new data. This method can be applied right away and may help front-line doctors make clinical decisions in situations where there are limited resources and time.  \nKeywords: big data study, data mining research, machine learning algorithm, prediction models  \n1. INTRODUCTION  \nClinical Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), the responsible agent of novel coronavirus (COVID-19 or 2019-nCoV), appeared in late 2019 and likely to come from Hubei Province, China called Wuhan[1][2] It is suspected that COVID-19, which is quickly spreading in humans, was initially originated from bats and likely spread to humans through intermediate hosts, the raccoon dog (Nyctereutes procyonoides) and palm civet (Paguma larvata) [3][4] The earliest symptom of SARS-COV-2 were fever, coughing, and shortness of breath, which frequently matched the flu. [2] Since then, COVID-19 advanced to a critical stage and spread globally, infecting numerous people. Human-to-human transmission of COVID-19 from infected patients with moderate symptoms has also been documented. [5]. Nevertheless, no drug or vaccine has been clinically shown to cure COVID-19 pandemic, so other non-clinical or non-medical therapeutic techniques, such as data mining techniques, machine learning, and expert systems, among other artificial intelligence techniques, are needed to contain and prevent further outbreak.  \nData Mining (DM) is a sophisticated AI methods for finding new, practical, and reliable hidden patterns or knowledge from datasets. [6] The method identifies connections, information, or patterns between the datasets in multiple or a specific datas","cbCaivyIr2S68tZL","https://ap.wps.com/l/cbCaivyIr2S68tZL","pdf",438219,1,12,"English","en",105,"# Abstract\n# Introduction\n## Data mining for healthcare decision support\n# Methodology\n## Dataset collection and description\n## Data mining techniques and algorithms\n### Logistic Regression (LR)","[{\"question\":\"Which machine learning algorithms were compared for COVID-19 mortality prediction?\",\"answer\":\"The study compared six supervised algorithms: KNN, Naive Bayes, SVM, decision tree, random forest, and logistic regression, evaluated with 10-fold cross-validation.\"},{\"question\":\"What variables were identified as top mortality predictors?\",\"answer\":\"The top predictors reported were Mean Artery Pressure (MAP), stroke, age, and IL6, with statistically significant associations.\"},{\"question\":\"How well did the best model perform and what evaluation metrics were used?\",\"answer\":\"Logistic regression was reported as the best model, achieving about 81% ROC. Performance was assessed using precision, recall, F-measure accuracy, and ROC AUC.\"}]","Multivariate Analysis and Machine Learning - Mortality Predictions In COVID-19 Patients from Comorbidity, Demographic and Laboratory Findings | PDF",1785899334,30,{"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},"multivariate-analysis-and-machine-learning-mortality-predictions-in-covid-19-patients-from-comorbidity-demographic-and-laboratory-findings","",{"@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/multivariate-analysis-and-machine-learning-mortality-predictions-in-covid-19-patients-from-comorbidity-demographic-and-laboratory-findings/125496/",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","2026-08-05",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},"Which machine learning algorithms were compared for COVID-19 mortality prediction?","Question",{"text":75,"@type":76},"The study compared six supervised algorithms: KNN, Naive Bayes, SVM, decision tree, random forest, and logistic regression, evaluated with 10-fold cross-validation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What variables were identified as top mortality predictors?",{"text":80,"@type":76},"The top predictors reported were Mean Artery Pressure (MAP), stroke, age, and IL6, with statistically significant associations.",{"name":82,"@type":73,"acceptedAnswer":83},"How well did the best model perform and what evaluation metrics were used?",{"text":84,"@type":76},"Logistic regression was reported as the best model, achieving about 81% ROC. 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