A Comparison of Machine Learning Models for Mucopolysaccharidosis Early Diagnosis: A Retrospective Cohort Study using UAE SEHA Electronic Medical Records

A Comparison of Machine Learning Models for Mucopolysaccharidosis Early Diagnosis: A Retrospective Cohort Study using UAE SEHA Electronic Medical Records | 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 A Comparison of Machine Learning Models for Mucopolysaccharidosis Early Diagnosis: A Retrospective Cohort Study using UAE SEHA Electronic Medical Records Aamna AlShehhi, Hiba Alblooshi, Ruba Fadul, Natnael Tumzghi, Amal Al Tenaiji, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4780346/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Rare diseases, such as Mucopolysaccharidosis (MPS), present unique challenges to the healthcare system. Some of the most critical challenges are the delay and the lack of accurate disease diagnosis. Early diagnosis of MPS is crucial, as it has the potential to significantly improve patients' response to treatment, thereby reducing the risk of complications or death. This study aims to compare the performance of different machine learning (ML) models for MPS diagnosis using electronic health records (EHR) from the Abu Dhabi Health Services Company (SEHA). Our retrospective cohort study consists of 115 registered patients whose age <= 19 Years old from 2004 to 2022. Using nested cross-validation, we trained different feature extraction algorithms in combination with various ML algorithms, and we evaluated them using various evaluation metrics. Finally, the models with the highest performance were further interpreted using Shapley additive explanations (SHAP). We found that Naive Bayes trained on the domain expert features reported the highest performance: accuracy: 0.93 (0.08), AUC: 0.96(0.04), F1-score: 0.91(0.1), and MCC: 0.86 (0.16). The top reported features were acute pharyngitis, accretions on teeth, and body mass index pediatric, greater than or equal to the 95th percentile for age. This study offers a cost-effective screening method for MPS patients using non-invasive EHR. Physical sciences/Engineering/Biomedical engineering Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Dec, 2024 Reviews received at journal 26 Dec, 2024 Reviewers agreed at journal 05 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviewers invited by journal 19 Aug, 2024 Editor assigned by journal 19 Aug, 2024 Editor invited by journal 06 Aug, 2024 Submission checks completed at journal 29 Jul, 2024 First submitted to journal 22 Jul, 2024 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. 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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-4780346","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":343925019,"identity":"6a9ba3f5-c56f-4e53-8a6e-f7c19fdf64eb","order_by":0,"name":"Aamna 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