Predicting the Risk of Surgical Complications Using Machine Learning Models

Predicting the Risk of Surgical Complications Using Machine Learning Models | 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 Predicting the Risk of Surgical Complications Using Machine Learning Models Dheiver Francisco Santos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5426691/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 Predicting the risk of surgical complications is essential to improve patient outcomes and optimize healthcare resources. In this paper, we propose the application of machine learning (ML) techniques to predict surgical risks based on pre-operative data. We used three supervised learning algorithms: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). A stacked ensemble model combining these algorithms was also explored to enhance the prediction accuracy. The proposed ensemble model achieved a prediction accuracy of 94 Artificial Intelligence and Machine Learning Surgical Complications Machine Learning En-semble Learning Logistic Regression Random Forest Support Vector Machine Prediction Healthcare Full Text Additional Declarations The authors declare no competing interests. Dheiver Francisco Santos, one of the authors, is employed by Cognai. This affiliation does not compromise the scientific integrity of the findings presented. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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