Leakage-Safe Benchmarking of Tempered Fractional Optimization and Swarm-Driven Feature Construction for Heart Disease Prediction on Structured Clinical Data

Leakage-Safe Benchmarking of Tempered Fractional Optimization and Swarm-Driven Feature Construction for Heart Disease Prediction on Structured Clinical Data | 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 Leakage-Safe Benchmarking of Tempered Fractional Optimization and Swarm-Driven Feature Construction for Heart Disease Prediction on Structured Clinical Data Intissar DABBACHI, Sabeur MASMOUDI, Imed Bouzida, Omar NAIFAR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9447779/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Prediction of heart disease from structured clinical data requires both predictive performance and methodological rigor. This paper presents a leakage-safe benchmark of tempered fractional optimization and swarm-driven feature construction for heart disease prediction. The proposed framework combines a tempered fractional gradient-based logistic learner with particle swarm optimization (PSO)-driven nonlinear feature construction and is evaluated against tuned strong baselines, including elastic-net logistic regression, Extra Trees, histogram-based gradient boosting, XGBoost, LightGBM, and a stacking ensemble. The experimental protocol nests preprocessing, hyperparameter tuning, and PSO construction strictly within training data to avoid leakage. Across 15 repeated stratified hold-out splits, the proposed TFGD_PSO consistently improves over plain TFGD. However, the stacking ensemble achieves the strongest overall performance (mean ROC-AUC 0.9318 ± 0.0146, mean F1-score 0.8917±0.0210, mean MCC 0.7564±0.0463), while Extra Trees reaches the highest mean PR-AUC. TFGD_PSO remains competitive and computationally attractive. The study provides a rigorous benchmark showing that PSO-enhanced tempered fractional learning improves over plain tempered fractional optimization, while tuned tree-based ensembles remain the strongest predictors for this dataset. heart disease prediction tempered fractional optimization particle swarm optimization tabular machine learning leakage-safe evaluation ensemble learning calibration medical artificial intelligence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 17 Apr, 2026 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-9447779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638086867,"identity":"3f0bfc3d-844a-4ffe-b462-0dfb7e8cd37d","order_by":0,"name":"Intissar DABBACHI","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Intissar","middleName":"","lastName":"DABBACHI","suffix":""},{"id":638086868,"identity":"87dc106f-dfd4-46d6-a6f4-9303929a555a","order_by":1,"name":"Sabeur MASMOUDI","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Sabeur","middleName":"","lastName":"MASMOUDI","suffix":""},{"id":638086869,"identity":"17cd895f-6a93-4ab2-b5ee-b011175375e1","order_by":2,"name":"Imed Bouzida","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACNiCWYGwAkswMBgwfQCLspGhhnAESYSbCJogWBgYDZh4GsF78gE/s8MEbjDtsEue3M2/8bPNrmzwfMwPjh485eBwmnZZswXgmLbGxma1YOrfvtmEbMwOz5Mxt+LTkmEkwth1ObGbmMZDO7bnNCNTCxsyLV0v+N7CWNmYe49+WPbftidCSwwbW0sPMYybN8ON2IhFa0owtEs+kGc9gZiuz7G24ndzGzNiM1y/ys5Mf3vi4w0Z2fv/hzTd+/LltO7+9+eCHj3i0gEECjMHYBiYbCKhHAX9IUTwKRsEoGAUjBQAAKtFImluYloMAAAAASUVORK5CYII=","orcid":"","institution":"Abu Dhabi University","correspondingAuthor":true,"prefix":"","firstName":"Imed","middleName":"","lastName":"Bouzida","suffix":""},{"id":638086870,"identity":"d7f5f0c3-c154-4d45-be57-39bb8ec69fa6","order_by":3,"name":"Omar NAIFAR","email":"","orcid":"","institution":"University of Sfax","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"","lastName":"NAIFAR","suffix":""}],"badges":[],"createdAt":"2026-04-17 10:23:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9447779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9447779/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109036312,"identity":"f6c761a2-9fe8-4b58-8265-a95fd4c44ce5","added_by":"auto","created_at":"2026-05-12 02:36:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3515166,"visible":true,"origin":"","legend":"","description":"","filename":"ALeakageSafeGeneticAlgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9447779/v1_covered_69507292-70ff-495e-9ccf-a499e2ee3a7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leakage-Safe Benchmarking of Tempered Fractional Optimization and Swarm-Driven Feature Construction for Heart Disease Prediction on Structured Clinical Data","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":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"heart disease prediction, tempered fractional optimization, particle swarm optimization, tabular machine learning, leakage-safe evaluation, ensemble learning, calibration, medical artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-9447779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9447779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Prediction of heart disease from structured clinical data requires both predictive performance and methodological rigor. This paper presents a leakage-safe benchmark of tempered fractional optimization and swarm-driven feature construction for heart disease prediction. The proposed framework combines a tempered fractional gradient-based logistic learner with particle swarm optimization (PSO)-driven nonlinear feature construction and is evaluated against tuned strong baselines, including elastic-net logistic regression, Extra Trees, histogram-based gradient boosting, XGBoost, LightGBM, and a stacking ensemble. The experimental protocol nests preprocessing, hyperparameter tuning, and PSO construction strictly within training data to avoid leakage. Across 15 repeated stratified hold-out splits, the proposed TFGD_PSO consistently improves over plain TFGD. However, the stacking ensemble achieves the strongest overall performance (mean ROC-AUC 0.9318 ± 0.0146, mean F1-score 0.8917±0.0210, mean MCC 0.7564±0.0463), while Extra Trees reaches the highest mean PR-AUC. 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