Benchmarking Outlier Detection Algorithms in Healthcare: An Analysis Across Diverse Medical Datasets

Benchmarking Outlier Detection Algorithms in Healthcare: An Analysis Across Diverse Medical Datasets | 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 Benchmarking Outlier Detection Algorithms in Healthcare: An Analysis Across Diverse Medical Datasets Jaloliddin Rustamov, Zahiriddin Rustamov, Nadia Badawi, Rafat Damseh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5242162/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 Outlier detection plays a vital role in data analysis by identifying data points that differ from the expected patterns. In healthcare, this is especially important, as spotting these anomalies can help detect unusual patient conditions, errors in data, or even early signs of fraud. However, even though this is crucial, little research has compared how well different outlier detection methods work in healthcare. This study aims to fill that gap by testing 37 different outlier detection algorithms on 28 healthcare datasets, covering everything from common conditions like diabetes to rare diseases like thyroid disorders. We measured their performance using metrics like ROC-AUC, Average Precision, and F1 Score, along with practical considerations like training time, prediction speed, and memory usage. Our results show that no single model works best across all scenarios. However, methods like HBOS, COPOD, and IForest achieved average ROC-AUC scores of 0.85, 0.83, and 0.82, respectively, performing well in various situations. More complex models like AnoGAN and DeepSVDD achieved ROC-AUC scores up to 0.90 on specific datasets, particularly excelling in datasets with higher dimensionality. This variety highlights the need to choose outlier detection methods based on the specific healthcare context, balancing accuracy and efficiency to meet different clinical and practical needs.This paper provides a solid foundation for future research and development of outlier detection systems in healthcare. Health sciences/Health care Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFilesforBenchmarkingOutlierDetectionAlgorithmsinHealthcareAnAnalysisAcrossDiverseMedicalDatasets.pdf 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-5242162","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376928114,"identity":"e29ae9c2-9b23-4fb0-81ec-5f8e4083fe49","order_by":0,"name":"Jaloliddin 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In healthcare, this is especially important, as spotting these anomalies can help detect unusual patient conditions, errors in data, or even early signs of fraud. However, even though this is crucial, little research has compared how well different outlier detection methods work in healthcare. This study aims to fill that gap by testing 37 different outlier detection algorithms on 28 healthcare datasets, covering everything from common conditions like diabetes to rare diseases like thyroid disorders. We measured their performance using metrics like ROC-AUC, Average Precision, and F1 Score, along with practical considerations like training time, prediction speed, and memory usage. Our results show that no single model works best across all scenarios. However, methods like HBOS, COPOD, and IForest achieved average ROC-AUC scores of 0.85, 0.83, and 0.82, respectively, performing well in various situations. More complex models like AnoGAN and DeepSVDD achieved ROC-AUC scores up to 0.90 on specific datasets, particularly excelling in datasets with higher dimensionality. This variety highlights the need to choose outlier detection methods based on the specific healthcare context, balancing accuracy and efficiency to meet different clinical and practical needs.This paper provides a solid foundation for future research and development of outlier detection systems in healthcare.","manuscriptTitle":"Benchmarking Outlier Detection Algorithms in Healthcare: An Analysis Across Diverse Medical Datasets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 06:14:11","doi":"10.21203/rs.3.rs-5242162/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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}}],"origin":"","ownerIdentity":"e8fb7a2d-366d-4d46-af53-5c9e8f9c376e","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40124032,"name":"Health sciences/Health care"},{"id":40124033,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2025-01-17T09:39:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 06:14:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5242162","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5242162","identity":"rs-5242162","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}