Analysis On Mortality Rate Due to Sepsis in Different Demography

Analysis On Mortality Rate Due to Sepsis in Different Demography | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Analysis On Mortality Rate Due to Sepsis in Different Demography N Smitha, R Tanuja, SH Manjula This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6914717/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract The incidence of sepsis, which accounts for up to 6% of all hospital admissions, is estimated to be between 250 and 500 cases per 10,000 people per year. Sepsis is the condition that costs the healthcare system the most and has been identified as one of the most pressing issues affecting global health. So, our study provides a solution to analyze the chances of the occurrence of sepsis by measuring the SIRS Score of the patients using their physiological characteristics of the patients, such as heart rate, respiratory rate, white blood cell count and body temperature. Our model is able to predict the outcome as dead or alive according to certain characteristics so that healthcare specialists can prioritize treatment for patients accordingly. Due to the highly imbalanced nature of the dataset, several techniques such as Random Oversampling and Random Undersampling were applied to address class imbalance. A Logistic Regression classifier was trained, and performance was evaluated using various metrics, including accuracy , precision, recall, F1-score, and the confusion matrix. Initially, the model showed high precision but poor recall for the sepsis class, which was improved by adjusting the decision threshold from 0.5 to 0.4. Cross-validation and AUC score evaluations demonstrated a solid model performance, with AUC consistently above 0.7. To further enhance the model, hyperparameter tuning and alternative models like Random Forest and XGBoost were considered. The findings highlight the importance of threshold adjustment, cross-validation, and feature 1 selection for improving model performance, particularly in imbalanced classification tasks. Further improvements could be made by fine-tuning hyperparameters and exploring additional machine learning models. Health sciences/Diseases Physical sciences/Engineering Infection Logistic Regression Mortality Sepsis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 21 Nov, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviews received at journal 19 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 02 Aug, 2025 Editor assigned by journal 02 Aug, 2025 Editor invited by journal 07 Jul, 2025 Submission checks completed at journal 01 Jul, 2025 First submitted to journal 01 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6914717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":495271597,"identity":"00580f56-c886-446d-85d8-e49c6c0ac634","order_by":0,"name":"N Smitha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFACxgZmICkDxEC6AkQxNxDS0tgMJHkgWs6AKEZCWhgYEVoY2yD24lUvP+1w++OCmjs8/A3Mj41uzquN5m8HavlRsQ2nFoPbiY3NM44945E4wGacnLvteO6Mw4wNjD1nbuPWIg3UwsN2mIfhAA/z4dxtx3IbgFqYGdtwa5GfDdLy7zCPPFjLnGO58wlpYQA5jLftMI8BUEtybkNN7gZCWkB+mc3bd5jH8DCbsXHOsQO5G4FaDuLzi/zs9Aefeb4dlpM73vxYOqemLnfe+cMHH/yowOMwOGAGk4fB5AEi1MNBHSmKR8EoGAWjYIQAAD3YWvHbmcEAAAAAAElFTkSuQmCC","orcid":"","institution":"University Visvesvaraya College of Engineering, Bangalore University","correspondingAuthor":true,"prefix":"","firstName":"N","middleName":"","lastName":"Smitha","suffix":""},{"id":495271598,"identity":"3b0dad48-7aa5-4fd2-9824-703780b149d3","order_by":1,"name":"R Tanuja","email":"","orcid":"","institution":"University Visvesvaraya College of Engineering, Bangalore University","correspondingAuthor":false,"prefix":"","firstName":"R","middleName":"","lastName":"Tanuja","suffix":""},{"id":495271603,"identity":"78b1a828-8dd7-4fe8-88ef-e62898857aaa","order_by":2,"name":"SH Manjula","email":"","orcid":"","institution":"University Visvesvaraya College of Engineering, Bangalore University","correspondingAuthor":false,"prefix":"","firstName":"SH","middleName":"","lastName":"Manjula","suffix":""}],"badges":[],"createdAt":"2025-06-17 13:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6914717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6914717/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88582773,"identity":"47423bf1-f2f8-4cec-b644-27022a82a1be","added_by":"auto","created_at":"2025-08-08 03:20:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":765173,"visible":true,"origin":"","legend":"","description":"","filename":"SmiP2JournalofHealthcareInformaticsResearch5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6914717/v1_covered_9ad16cb5-ea50-4d22-82ae-599d5a0c75bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis On Mortality Rate Due to Sepsis in Different Demography","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Infection, Logistic Regression, Mortality, Sepsis","lastPublishedDoi":"10.21203/rs.3.rs-6914717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6914717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The incidence of sepsis, which accounts for up to 6% of all hospital admissions, is estimated to be between 250 and 500 cases per 10,000 people per year. 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Initially, the model showed high precision but poor recall for the sepsis class, which was improved by adjusting the decision threshold from 0.5 to 0.4. Cross-validation and AUC score evaluations demonstrated a solid model performance, with AUC consistently above 0.7. To further enhance the model, hyperparameter tuning and alternative models like Random Forest and XGBoost were considered. The findings highlight the importance of threshold adjustment, cross-validation, and feature 1 selection for improving model performance, particularly in imbalanced classification tasks. 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