Towards Transparent AI-Aided Neurology: Detection and Lateralization of Parkinson's Disease

Towards Transparent AI-Aided Neurology: Detection and Lateralization of Parkinson's Disease | 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 Towards Transparent AI-Aided Neurology: Detection and Lateralization of Parkinson's Disease First Bouslah Ayoub, Second Mekhilef Ilyes, Third Taleb Nora, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7528990/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background: Parkinson's Disease (PD) diagnosis remains challenging due to subjective assessments and delayed detection of asymmetric motor symptoms. While digital biomarkers like keystroke dynamics show promise, most AI approaches lack clinical interpretability and fail to lateralize motor onset. Methods: We propose a transparent machine learning framework named KATE using keystroke data from the Tappy keyboard app. Hand-specific features (hold-time variance, inter-key intervals, asymmetry indices) were engineered to quantify motor laterality. Four models—Logistic Regression, SVM, Random Forest, and XGBoost—were evaluated for binary PD detection and multiclass asymmetry classification (left/right-dominant, symmetric). SHAP and LIME provided global/local explanations. Results: Binary detection: XGBoost achieved near-perfect performance (AUC=1.00, recall=99.6\%, FP rate=4.3\%).\\ Asymmetry classification: XGBoost led with F1=85\% and precision=83\%, though right-dominant PD recall lagged (78\%). Explainability: SHAP identified pathophysiological biomarkers—Latency time\_max (motor initiation delay), Flight\_min\_RS (inter-key coordination deficits), and Hold\_std\_S (motion smoothness degradation). Ensembles reduced false positives by 42\% versus Logistic Regression. Conclusion: Our framework enables accurate, asymmetry-aware PD screening while providing clinically interpretable insights. Integration of hand-specific digital phenotyping with explainable AI pioneers a template for transparent neurodegenerative disease diagnostics. Parkinson’s Disease Detection Explainable Artificial Intelligence Motor Asymmetry Lateralization Ensemble Learning Digital Biomarkers Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 06 Sep, 2025 Editor assigned by journal 06 Sep, 2025 Submission checks completed at journal 06 Sep, 2025 First submitted to journal 03 Sep, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7528990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513850702,"identity":"b7701196-2cb6-4b1d-b8e1-94809c67fe03","order_by":0,"name":"First Bouslah Ayoub","email":"data:image/png;base64,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","orcid":"","institution":"LISCO Laboratory Of Badji Mokhtar University","correspondingAuthor":true,"prefix":"","firstName":"First","middleName":"Bouslah","lastName":"Ayoub","suffix":""},{"id":513850703,"identity":"2ae2b563-6110-4e58-9c2e-a8eecbad3f5f","order_by":1,"name":"Second Mekhilef Ilyes","email":"","orcid":"","institution":"LISCO Laboratory Of Badji Mokhtar University","correspondingAuthor":false,"prefix":"","firstName":"Second","middleName":"Mekhilef","lastName":"Ilyes","suffix":""},{"id":513850704,"identity":"b87731de-fc12-474e-988a-258339d2486c","order_by":2,"name":"Third Taleb Nora","email":"","orcid":"","institution":"LISCO Laboratory Of Badji Mokhtar University","correspondingAuthor":false,"prefix":"","firstName":"Third","middleName":"Taleb","lastName":"Nora","suffix":""},{"id":513850705,"identity":"f85357d4-d1d3-425a-943b-f0f3254038b7","order_by":3,"name":"Fourth Khaoucha Aicha","email":"","orcid":"","institution":"National Higher School of Technology and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Fourth","middleName":"Khaoucha","lastName":"Aicha","suffix":""}],"badges":[],"createdAt":"2025-09-03 16:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7528990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7528990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91160386,"identity":"380e2f07-58e3-4461-bdf8-8ffb75a0da8c","added_by":"auto","created_at":"2025-09-12 09:03:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":872388,"visible":true,"origin":"","legend":"","description":"","filename":"TowardsTransparentAIAidedNeurologyDetectionandLateralizationofParkinsonsDisease1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7528990/v1_covered_1461f472-6ef0-44a6-a299-e0b59cfca9f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards Transparent AI-Aided Neurology: Detection and Lateralization of Parkinson's Disease","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"biomedical-materials-and-devices","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Biomedical Materials \u0026 Devices](https://link.springer.com/journal/44174)","snPcode":"44174","submissionUrl":"https://submission.springernature.com/new-submission/44174/3","title":"Biomedical Materials \u0026 Devices","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Parkinson’s Disease Detection, Explainable Artificial Intelligence, Motor Asymmetry Lateralization, Ensemble Learning, Digital Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7528990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7528990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Parkinson's Disease (PD) diagnosis remains challenging due to subjective assessments and delayed detection of asymmetric motor symptoms. While digital biomarkers like keystroke dynamics show promise, most AI approaches lack clinical interpretability and fail to lateralize motor onset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: We propose a transparent machine learning framework named KATE using keystroke data from the Tappy keyboard app. Hand-specific features (hold-time variance, inter-key intervals, asymmetry indices) were engineered to quantify motor laterality. Four models—Logistic Regression, SVM, Random Forest, and XGBoost—were evaluated for binary PD detection and multiclass asymmetry classification (left/right-dominant, symmetric). SHAP and LIME provided global/local explanations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Binary detection: XGBoost achieved near-perfect performance (AUC=1.00, recall=99.6\\%, FP rate=4.3\\%).\\\\ Asymmetry classification: XGBoost led with F1=85\\% and precision=83\\%, though right-dominant PD recall lagged (78\\%). Explainability: SHAP identified pathophysiological biomarkers—Latency time\\_max (motor initiation delay), Flight\\_min\\_RS (inter-key coordination deficits), and Hold\\_std\\_S (motion smoothness degradation). Ensembles reduced false positives by 42\\% versus Logistic Regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Our framework enables accurate, asymmetry-aware PD screening while providing clinically interpretable insights. 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