SHAP-LR: An Interpretable Logistic Regression Model for Coronary Heart Disease Risk Prediction

SHAP-LR: An Interpretable Logistic Regression Model for Coronary Heart Disease Risk Prediction | 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 SHAP-LR: An Interpretable Logistic Regression Model for Coronary Heart Disease Risk Prediction Peihua Tong, Hui Hu, Ling Tong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6491762/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 Introduction : Coronary Heart Disease (CHD) is a global leading cause of death, demanding accurate predictive tools for early intervention. This study develops an interpretable machine learning framework (SHAP-LR) for CHD risk prediction, combining feature engineering, logistic regression, and SHAP-based explainability to support clinical decision-making. Methods : The study employed three publicly accessible datasets: BRFSS_2015, Cleveland, Hungary, Switzerland, VA Long Beach, and Stalog (Heart) datasets. Data preprocessing involved cleaning, standardization, and feature selection, with SHAP values used to enhance interpretability. Multiple machine learning models, including Decision Tree, AdaBoost, Gradient Boost, Bagging, CatBoost, Extra Trees, and Logistic Regression, were evaluated. Model performance was assessed using accuracy, precision, recall, F1-score, and AUROC. A user-friendly HD scoring system was developed based on the best-performing model, logistic regression, which was further optimized through hyperparameter tuning. Results : Logistic regression outperformed other models, achieving an accuracy of 90.54% and an AUROC of 80.27% on the BRFSS_2015 dataset. After hyperparameter tuning, the model's performance improved further, with accuracy reaching 90.55% and AUROC increasing to 81.09%. The SHAP value analysis revealed that age, high blood pressure, and high cholesterol were the most significant predictors of HD. The developed scoring system provided a quantifiable risk assessment tool, enabling clinicians to stratify patients based on their HD risk effectively. Conclusion : This study demonstrates the effectiveness of machine learning in predicting heart disease, with logistic regression emerging as the most reliable model. The integration of SHAP values enhanced the model's interpretability, making it a valuable tool for clinical decision-making. The developed HD scoring system offers a practical and efficient method for risk assessment, potentially improving early diagnosis and intervention strategies. Future work will focus on validating the model in diverse clinical settings and exploring additional features to further enhance predictive accuracy. Heart Disease Machine Learning Logistic Regression SHAP Values Risk Assessment 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. 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-6491762","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467072322,"identity":"fa6ba7ac-6995-466c-b284-8369c052d336","order_by":0,"name":"Peihua Tong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACxgbmxsc/Kv7V2zczNj5IqKghRgtjszHDmQMJBuzMhw0enDlGlD1t0oxtQC38bGmSD1uYCWtgnpHYbFzAdifPnJnHrCKxgY2Bv707Ab8dPQcbH8/geVZs2cxjdiNxhwyDxJmzG/BraW9sNuCRYGZsOAzScoaNwUAil4CWZsY2CR4DiJaCxDZmIrS0N7ZJ8yQcTtxwmC2NgTgtPQebDWccSDOWbGY+LJFw5hgPQb8Yzkg++ODjPxs5fv6DjR9/VNTI8bf3EtDSgCbAg1c5CMgTVDEKRsEoGAWjAACTDU5DNIp61AAAAABJRU5ErkJggg==","orcid":"","institution":"Huzhou Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Peihua","middleName":"","lastName":"Tong","suffix":""},{"id":467072323,"identity":"abd2aa7d-f26b-4e33-94bc-ecd21478a0d1","order_by":1,"name":"Hui Hu","email":"","orcid":"","institution":"Huzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Hu","suffix":""},{"id":467072324,"identity":"6d0654f5-0d16-43b0-a954-2ebd4db2b94b","order_by":2,"name":"Ling Tong","email":"","orcid":"","institution":"Huzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Tong","suffix":""}],"badges":[],"createdAt":"2025-04-21 02:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6491762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6491762/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93569968,"identity":"1baafa50-5fa1-4038-9a48-713ef1e8ea05","added_by":"auto","created_at":"2025-10-15 08:55:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":638954,"visible":true,"origin":"","legend":"","description":"","filename":"SHAPLRAnInterpretableLogisticRegressionModelforHeartDiseaseRiskPrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6491762/v1_covered_cb55ab00-8de1-46df-a366-15372ee8a5c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SHAP-LR: An Interpretable Logistic Regression Model for Coronary Heart Disease Risk Prediction","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":"Heart Disease, Machine Learning, Logistic Regression, SHAP Values, Risk Assessment","lastPublishedDoi":"10.21203/rs.3.rs-6491762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6491762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eIntroduction\u003c/b\u003e: Coronary Heart Disease (CHD) is a global leading cause of death, demanding accurate predictive tools for early intervention. This study develops an interpretable machine learning framework (SHAP-LR) for CHD risk prediction, combining feature engineering, logistic regression, and SHAP-based explainability to support clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: The study employed three publicly accessible datasets: BRFSS_2015, Cleveland, Hungary, Switzerland, VA Long Beach, and Stalog (Heart) datasets. Data preprocessing involved cleaning, standardization, and feature selection, with SHAP values used to enhance interpretability. Multiple machine learning models, including Decision Tree, AdaBoost, Gradient Boost, Bagging, CatBoost, Extra Trees, and Logistic Regression, were evaluated. Model performance was assessed using accuracy, precision, recall, F1-score, and AUROC. A user-friendly HD scoring system was developed based on the best-performing model, logistic regression, which was further optimized through hyperparameter tuning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: Logistic regression outperformed other models, achieving an accuracy of 90.54% and an AUROC of 80.27% on the BRFSS_2015 dataset. After hyperparameter tuning, the model's performance improved further, with accuracy reaching 90.55% and AUROC increasing to 81.09%. The SHAP value analysis revealed that age, high blood pressure, and high cholesterol were the most significant predictors of HD. The developed scoring system provided a quantifiable risk assessment tool, enabling clinicians to stratify patients based on their HD risk effectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e: This study demonstrates the effectiveness of machine learning in predicting heart disease, with logistic regression emerging as the most reliable model. The integration of SHAP values enhanced the model's interpretability, making it a valuable tool for clinical decision-making. The developed HD scoring system offers a practical and efficient method for risk assessment, potentially improving early diagnosis and intervention strategies. Future work will focus on validating the model in diverse clinical settings and exploring additional features to further enhance predictive accuracy.\u003c/p\u003e","manuscriptTitle":"SHAP-LR: An Interpretable Logistic Regression Model for Coronary Heart Disease Risk Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 07:31:38","doi":"10.21203/rs.3.rs-6491762/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":"1e2016dd-3d22-431e-b370-1fd99f0f6ef6","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-15T08:54:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 07:31:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6491762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6491762","identity":"rs-6491762","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}