Explainable AI-Based Coronary Heart Disease Prediction: Enhancing Model Transparency in Clinical Decision Making

Explainable AI-Based Coronary Heart Disease Prediction: Enhancing Model Transparency in Clinical Decision Making | 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 Explainable AI-Based Coronary Heart Disease Prediction: Enhancing Model Transparency in Clinical Decision Making Avichandra Singh Ningthoujam, Shilpa Sharma, Avishak Nandi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6428386/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 Purpose: Coronary heart disease (CHD) is still a major cause of death globally, and hence early detection and risk stratification are necessary to avoid major cardiovascular events. The present study uses clinical and demographic characteristics to compare the predictive accuracy of eight machine learning models for CHD diagnosis. It also investigates the contribution and direction of influence of the most important features in the models to improve interpretability. Methods: We contrasted the predictive accuracy of eight different machine learning models for CHD classification. The work identifies the most important features from the top-performing models and applies SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to gain insight into how every feature affects the model's prediction. These interpretive methods assist in displaying the direction and amount of feature contributions to allow transparency in AI-based CHD risk prediction. Results: XGboost and Random Forest achieved the highest testing accuracies, 0.839 and 0.805, with training accuracies of 0.901 and 0.95,7, respectively, showing an ideal model of XGboost and significant overfitting for the random forest model. ECG-associated features, such as resting ECG and old peak (ST depression through workout), also place favorably, supporting the significance of cardiac electrical activity in diagnosis. ST slope has the highest impact, followed by Chest pain type and old peak, which increase the likelihood of heart disease with their high values contributing positively to the prediction. Resting BPs, sex, and fasting blood sugar have lower impacts on the model's predictions. Conclusions: In conclusion, machine learning models, particularly XGboost and random forest, show substantial predictive accuracy for coronary heart disease, with testing AUROCs of 0.885. Feature importance, SHAP, and LIME analysis highlight the critical role of ECG-derived metrics like ST slope, chest pain type, and resting ecp while traditional risk factors such as cholesterol, resting bps, and fasting blood sugar have less influence. Coronary Heart Disease Explainable AI Machine Learning SHAP Transparency 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-6428386","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452344370,"identity":"68c3aa69-57d1-44cc-90ad-a0e37ba6273d","order_by":0,"name":"Avichandra Singh Ningthoujam","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Avichandra","middleName":"Singh","lastName":"Ningthoujam","suffix":""},{"id":452344371,"identity":"99e14b5d-41ba-45ec-8fa7-ac6c2e33b4be","order_by":1,"name":"Shilpa Sharma","email":"data:image/png;base64,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","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":true,"prefix":"","firstName":"Shilpa","middleName":"","lastName":"Sharma","suffix":""},{"id":452344372,"identity":"4561c68e-51bb-4436-973a-6cce4232c742","order_by":2,"name":"Avishak Nandi","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Avishak","middleName":"","lastName":"Nandi","suffix":""}],"badges":[],"createdAt":"2025-04-11 12:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6428386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6428386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82551699,"identity":"afbebde5-b830-47db-bb79-b495436c5222","added_by":"auto","created_at":"2025-05-12 20:31:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":594999,"visible":true,"origin":"","legend":"","description":"","filename":"ExplainableAIBasedCoronaryHeartDiseasePredictionEnhancingModelTransparencyinClinicalDecisionMaking.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6428386/v1_covered_69ff60a4-151f-4de1-b95a-2a2529a1f6b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable AI-Based Coronary Heart Disease Prediction: Enhancing Model Transparency in Clinical Decision Making","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":"Coronary Heart Disease, Explainable AI, Machine Learning, SHAP, Transparency","lastPublishedDoi":"10.21203/rs.3.rs-6428386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6428386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose: Coronary heart disease (CHD) is still a major cause of death globally, and hence early detection and risk stratification are necessary to avoid major cardiovascular events. The present study uses clinical and demographic characteristics to compare the predictive accuracy of eight machine learning models for CHD diagnosis. It also investigates the contribution and direction of influence of the most important features in the models to improve interpretability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: We contrasted the predictive accuracy of eight different machine learning models for CHD classification. The work identifies the most important features from the top-performing models and applies SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to gain insight into how every feature affects the model's prediction. These interpretive methods assist in displaying the direction and amount of feature contributions to allow transparency in AI-based CHD risk prediction.\u003c/p\u003e\n\u003cp\u003eResults: XGboost and Random Forest achieved the highest testing accuracies, 0.839 and 0.805, with training accuracies of 0.901 and 0.95,7, respectively, showing an ideal model of XGboost and significant overfitting for the random forest model. ECG-associated features, such as resting ECG and old peak (ST depression through workout), also place favorably, supporting the significance of cardiac electrical activity in diagnosis. ST slope has the highest impact, followed by Chest pain type and old peak, which increase the likelihood of heart disease with their high values contributing positively to the prediction. Resting BPs, sex, and fasting blood sugar have lower impacts on the model's predictions.\u003c/p\u003e\n\u003cp\u003eConclusions: In conclusion, machine learning models, particularly XGboost and random forest, show substantial predictive accuracy for coronary heart disease, with testing AUROCs of 0.885. Feature importance, SHAP, and LIME analysis highlight the critical role of ECG-derived metrics like ST slope, chest pain type, and resting ecp while traditional risk factors such as cholesterol, resting bps, and fasting blood sugar have less influence.\u003c/p\u003e","manuscriptTitle":"Explainable AI-Based Coronary Heart Disease Prediction: Enhancing Model Transparency in Clinical Decision Making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 10:31:28","doi":"10.21203/rs.3.rs-6428386/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":"0444661f-640e-4dd4-ab6e-49c28c2205b5","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T20:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 10:31:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6428386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6428386","identity":"rs-6428386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}