Visualizing Type 2 Diabetes Prevalence: Localizing Model Feature Impacts

Visualizing Type 2 Diabetes Prevalence: Localizing Model Feature Impacts | 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 Visualizing Type 2 Diabetes Prevalence: Localizing Model Feature Impacts Youssef Sultan, Mohammad Hammad, Kelly Lester This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4596583/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 SHAP values have been a common approach used to understand machine learning model predictions by averaging the marginal contributions of each feature across every possible permutation of the feature set \cite{lundberg2017unified}. Our research provides a localized view of SHAP values contributing to Type 2 Diabetes (T2D) prevalence in the United States from 2012 - 2021 covering each year independently. Instead of visualizing SHAP feature importance across an entire geographical dataset using a beeswarm plot, our approach is more granular. We visualize individual SHAP values of Social Determinants of Health (SDOH) features by county on a Choropleth map. Additionally, we found that replacing geographic identifiers such as zipcode with precise latitude and longitude coordinates before applying KNN imputation reduced the MSE by 10%. Our visualization reveals how specific factors influence T2D prevalence at the county level using a non-linear machine learning model. By re-appending the initially preserved geographic identifiers for each record by index, we traced the contribution of each SHAP value back to its locality. Our approach opens up a new geographical vantage point of the mechanisms of model predictions, thereby identifying localized key factors influencing Type 2 Diabetes (T2D). This study extends the possibilities for tailored interventions and public health policies showing how some factors have varying predictive impact on an outcome at the geographic level. Geospatial Data Analysis Health Disparities Predictive Modeling in Healthcare Spatial Epidemiology Full Text Additional Declarations No competing interests reported. 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. 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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-4596583","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315864849,"identity":"5b8a5875-c920-4d94-a380-1575d27765ad","order_by":0,"name":"Youssef Sultan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYHACxgNAQg7CbAASB4jQcwCoyJiBgZlELYkNRGsxOH/G4PCHmjvp893PH3zwcweDHN+NBAJabuQYHDhw7FnuxjPJzIa9ZxiMJQlr4QFqYTucu7EhmU2asY0hcQNBLUCHHTjw73C6Yf9j9t9ALfWEtRwAOuxg2+EEeYlkNmaglgQDQlokb6QVHDjbd9hwg8RjY8neNgnDmWce4NfCd/7wxgcV3w7Ly/cnPvzws81Gnu84AVsUDsBdCKYk8CsHAfkGdMYoGAWjYBSMAnQAANPzUySygb3LAAAAAElFTkSuQmCC","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Youssef","middleName":"","lastName":"Sultan","suffix":""},{"id":315864850,"identity":"83edb2f0-fd5d-4449-a6c5-95a294fb665e","order_by":1,"name":"Mohammad Hammad","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Hammad","suffix":""},{"id":315864851,"identity":"c407d790-58e6-4429-92a2-11dbfa8ca28b","order_by":2,"name":"Kelly Lester","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kelly","middleName":"","lastName":"Lester","suffix":""}],"badges":[],"createdAt":"2024-06-18 01:51:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4596583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4596583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61081450,"identity":"5768cb20-4379-4c86-b6ba-6cf5f31fdd84","added_by":"auto","created_at":"2024-07-25 10:52:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1065760,"visible":true,"origin":"","legend":"","description":"","filename":"mdjhhypcbznnhkmbvmqcmzkmbdzbwqgs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4596583/v1_covered_0ee26188-4f96-448f-9b61-59ef8a8fb5a8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Visualizing Type 2 Diabetes Prevalence: Localizing Model Feature Impacts","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"[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},"keywords":"Geospatial Data Analysis, Health Disparities, Predictive Modeling in Healthcare, Spatial Epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-4596583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4596583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"SHAP values have been a common approach used to understand machine learning model predictions by averaging the marginal contributions of each feature across every possible permutation of the feature set \\cite{lundberg2017unified}. 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