{"paper_id":"1f7c04e1-b7cc-41e4-8143-e1ca0cf675a9","body_text":"Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm | 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 Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm Romela Preena This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4127736/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 Competent people are a valuable asset for strong businesses. The issue of retaining competent staff with expertise poses a challenge to business owners. Companies may incur losses due to employee turnover if they are unable to replace lost expertise and productivity. Consequently, this research suggests a new model that uses machine learning to forecast staff turnover. The datasets are collected from Kaggle resource. The dataset has been pre-processed using standard scalar with Label Encoding method. The dataset has been trained with ML algorithm. The best features are selected by using modified genetic algorithm (MGA). The classification has been done with KNN, Gradient Boosting and Extra tree classifier. Finally, the attrition prediction using optimized levy fruit fly optimization (OLFFO). The experimental results are compared with ML algorithms with classification metrics (Accuracy, Precision, recall and f-measure). Biological sciences/Neuroscience/Gliogenesis Biological sciences/Neuroscience/Gustatory system/Taste receptors Health sciences/Risk factors Employee Attrition Hybrid Models ML OLFFO Full Text Additional Declarations Yes there is potential Competing Interest. Machine Learning 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. 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-4127736\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":281322209,\"identity\":\"a2587307-fb82-4dfe-9ea7-16306b5a94be\",\"order_by\":0,\"name\":\"Romela Preena\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYHAD5gaJD0CKjZ14LYwNkjNAWphJ0SLNA7aNgDpz6cMHHxfU1CX2Sx9svG3za5s8HzMD44ePObi1WPalJRvPOHY4cWZfYrN1bt9twzZmBmbJmdtwazE4w2MmzcN2IHfDGcY26dye24xALWzMvHi18H//zfOvDqLFsue2PRFaeIAK2pghWhh+3E4kqMWyh81YmrfvcP3MHsZmy96G28ltzIzNeP1izsP88DPPtzpjfh7mgzd+/LltO7+9+eCHj/gchsJjbAOTDbjVY2hh+INX8SgYBaNgFIxQAAAm800mU3WaowAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0009-0009-8567-9262\",\"institution\":\"Avinuashilingam University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Romela\",\"middleName\":\"\",\"lastName\":\"Preena\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-03-19 06:20:23\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4127736/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4127736/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":77818760,\"identity\":\"da42a76d-1fe1-4511-b5b0-bae607ca61f8\",\"added_by\":\"auto\",\"created_at\":\"2025-03-05 19:32:26\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":633264,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"EmployeeAttritionpredictionupdate.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4127736/v1_covered_3fba636e-096f-40da-858d-9a0c11bf8782.pdf\"}],\"financialInterests\":\"\\u003cb\\u003eYes\\u003c/b\\u003e there is potential Competing Interest.\\nMachine Learning\",\"formattedTitle\":\"\\u003cp\\u003eTechniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm\\u003c/p\\u003e\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Employee Attrition, Hybrid Models, ML, OLFFO\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4127736/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4127736/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCompetent people are a valuable asset for strong businesses. 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