A Machine Learning Approach to Identifying Depression in Coronary Artery Disease Patients Using Radial Artery Pulse Wave Analysis

A Machine Learning Approach to Identifying Depression in Coronary Artery Disease Patients Using Radial Artery Pulse Wave Analysis | 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 Machine Learning Approach to Identifying Depression in Coronary Artery Disease Patients Using Radial Artery Pulse Wave Analysis Lyu Yi, Rui Chen, Hai-Xia Yan, Hai-Mei Wu, Yi-Qin Wang, Jin Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5164891/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Coronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cause of death. More than 200 studies have evaluated depression as a risk factor for cardiac events in patients with established CAD. There is an urgent need to develop objective, simple, and cost-effective techniques for the detection of potential depression in CAD patients using machine learning (ML). Methods 228 participants were divided into three groups: healthy, CAD, and depressed CAD. The raw data of pulse wave from those participants was collected. The data were de-noised, normalized, and analyzed using several applications. Seven ML classifiers were used to model the processed data, including Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extra Trees (ET), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), and Unbiased Boosting with Categorical Features (CatBoost). Results The ET classifier demonstrated the best classification performance. After tuning hyperparameters, the results performance evaluation on test set are: 0.8261 accuracy, 0.9187 AUC, 0.8245 recall, 0.8255 precision, 0.825 F1-score, and 0.7398 MCC. The top 10 feature importances of tuned ET model are h f /4 , t 3 / t max , t f /6 / t 4 , t f /5 , t 4 / t max , t max / t , w , As , t 4 / t 1 , t 3 / t 1 . The top 20 features of SHAP value are: t 3 / t max , t f /6 / t 4 , h f /4 , t 3 / t 1 , t 4 / t max , t f /5 , w / t max , w / t 1 , w , t max / t , t 4 / t 1 , h f /3 , t 5 / t max , As , h f /5 , h f /6 , t f /3 / t max , t f /6 / t 1 , t f /4 / t 1 , and h 4 . Conclusion Radial artery pulse wave can be used to identify healthy, CAD and depressed CAD participants by using ET classifier. This method provides a potential pathway to recognize depressed CAD patients by using an objective, simple, and cost-effective technique. Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Psychology Health sciences/Cardiology Coronary artery disease Depression Radial pulse wave Extra Trees Classifier Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Nov, 2024 Reviews received at journal 14 Nov, 2024 Reviews received at journal 07 Nov, 2024 Reviewers agreed at journal 06 Nov, 2024 Reviewers agreed at journal 31 Oct, 2024 Reviewers agreed at journal 31 Oct, 2024 Reviewers invited by journal 30 Oct, 2024 Editor assigned by journal 30 Oct, 2024 Editor invited by journal 21 Oct, 2024 Submission checks completed at journal 16 Oct, 2024 First submitted to journal 27 Sep, 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-5164891","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388050577,"identity":"6dda25dc-011b-4a1a-84a5-278184d92605","order_by":0,"name":"Lyu Yi","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lyu","middleName":"","lastName":"Yi","suffix":""},{"id":388050578,"identity":"3a5026cc-4510-4e0d-acd7-5b4bf6f4e3ce","order_by":1,"name":"Rui Chen","email":"","orcid":"","institution":"Suzhou Global 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Depression, Radial pulse wave, Extra Trees Classifier, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5164891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5164891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronary artery disease (CAD) is a major global cardiovascular health threat and the leading cause of death in many countries. The disease has a significant impact in China, where it has become the leading cause of death. More than 200 studies have evaluated depression as a risk factor for cardiac events in patients with established CAD. There is an urgent need to develop objective, simple, and cost-effective techniques for the detection of potential depression in CAD patients using machine learning (ML).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e228 participants were divided into three groups: healthy, CAD, and depressed CAD. The raw data of pulse wave from those participants was collected. The data were de-noised, normalized, and analyzed using several applications. Seven ML classifiers were used to model the processed data, including Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extra Trees (ET), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), and Unbiased Boosting with Categorical Features (CatBoost).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe ET classifier demonstrated the best classification performance. After tuning hyperparameters, the results performance evaluation on test set are: 0.8261 accuracy, 0.9187 AUC, 0.8245 recall, 0.8255 precision, 0.825 F1-score, and 0.7398 MCC. The top 10 feature importances of tuned ET model are \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/4\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/6\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/5\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e, \u003cem\u003ew\u003c/em\u003e, \u003cem\u003eAs\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e. The top 20 features of SHAP value are: \u003cem\u003et\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/6\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/4\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/5\u003c/sub\u003e, \u003cem\u003ew\u003c/em\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003ew\u003c/em\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003ew\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/3\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eAs\u003c/em\u003e, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/5\u003c/sub\u003e, \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/6\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/3\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/6\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e/4\u003c/sub\u003e/\u003cem\u003et\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, and \u003cem\u003eh\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRadial artery pulse wave can be used to identify healthy, CAD and depressed CAD participants by using ET classifier. This method provides a potential pathway to recognize depressed CAD patients by using an objective, simple, and cost-effective technique.\u003c/p\u003e","manuscriptTitle":"A Machine Learning Approach to Identifying Depression in Coronary Artery Disease Patients Using Radial Artery Pulse Wave Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-23 17:27:29","doi":"10.21203/rs.3.rs-5164891/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-26T03:46:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-14T07:58:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-07T13:23:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249679794113819474899820501074797792852","date":"2024-11-06T17:32:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47765655239169906716944815918342168317","date":"2024-10-31T06:34:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88690922699161738310025738072160074535","date":"2024-10-31T04:02:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-31T03:23:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-31T03:20:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-21T19:02:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-16T05:04:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-27T11:42:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92a5f2c3-1c9e-4f7d-8674-8a7c48d81c65","owner":[],"postedDate":"December 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":41351077,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"},{"id":41351078,"name":"Biological sciences/Psychology"},{"id":41351079,"name":"Health sciences/Cardiology"}],"tags":[],"updatedAt":"2025-03-12T21:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-23 17:27:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5164891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5164891","identity":"rs-5164891","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}