Advanced COVID-19 Severity Prediction with Differential Weibull Polar Lights Optimizer and Case Study Insights

Advanced COVID-19 Severity Prediction with Differential Weibull Polar Lights Optimizer and Case Study Insights | 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 Research Article Advanced COVID-19 Severity Prediction with Differential Weibull Polar Lights Optimizer and Case Study Insights Caibing Shang, Meifang Huang, Sudan Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5296895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to a global health crisis, creating an urgent need for accurate predictive models to forecast disease severity and assist in clinical decision-making. This study presents an innovative machine learning approach, the bDWPLO-FKNN model, to predict the severity of COVID-19 pneumonia in patients. The model integrates the differential Weibull polar lights optimizer (DWPLO), an enhancement of the polar lights optimizer (PLO) with the differential evolution operator and the Weibull flight operator, to perform effective feature selection. The DWPLO's performance was rigorously tested against IEEE CEC 2017 benchmark functions, proving its robust optimization capabilities. The binary version of DWPLO (bDWPLO) was then combined with the fuzzy K-nearest neighbors (FKNN) algorithm to form the predictive model. Utilizing a dataset from the People's Hospital Affiliated with Ningbo University, the model was trained to identify patients at risk of developing severe pneumonia due to COVID-19. The bDWPLO-FKNN model demonstrated exceptional predictive accuracy, with an accuracy of 84.036%, and specificity of 88.564%. The analysis highlighted key predictors, including albumin, albumin to globulin ratio, lactate dehydrogenase, urea nitrogen, gamma-glutamyl transferase, and inorganic phosphorus, which were significantly associated with disease severity. The integration of DWPLO with FKNN not only enhances feature selection but also improves the model's predictive power, offering a valuable tool for clinicians to assess patient risk and allocate healthcare resources effectively during the COVID-19 pandemic. COVID-19 Severity Prediction Polar Lights Optimizer Differential Evolution Weibull Flight Operator Fuzzy K-Nearest Neighbors Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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