Dynamic Preclinical Detection and Progression Prediction of Neurodegenerative Diseases Using Multi-Modal Deep Learning

Dynamic Preclinical Detection and Progression Prediction of Neurodegenerative Diseases Using Multi-Modal Deep Learning | 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 Dynamic Preclinical Detection and Progression Prediction of Neurodegenerative Diseases Using Multi-Modal Deep Learning Ayesha Jabbar, Huang Jianjun, Muhammad Kashif Jabbar, Asad Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7455494/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) continue to pose significant global health challenges, particularly due to their insidious progression and the critical need for early, preclinical diagnosis. In this study, we present a novel multimodal deep learning framework that integrates heterogeneous data sources omics, wearable sensor data, and environmental exposure metrics for robust disease prediction and personalized progression forecasting. Our architecture employs a Transformer based encoder for omics data, Long Short-Term Memory (LSTM) networks for temporal modeling of wearable signals, and Graph Neural Networks (GNNs) to capture spatial correlations in environmental exposures. A trainable attention-based fusion mechanism dynamically integrates modality specific embeddings to generate a unified diagnostic representation. The framework was rigorously evaluated on benchmark datasets and achieved a classification accuracy of 98%, with sensitivity, specificity, and F1-score reaching 96%, 95%, and 97%, respectively. Furthermore, the model accurately predicted therapeutic windows with 93% precision, highlighting its potential for early clinical intervention. These results demonstrate the effectiveness of multimodal integration in enhancing diagnostic precision. Future extensions will explore federated learning to enable privacy-preserving and scalable deployment in real-world healthcare systems. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Biological sciences/Neuroscience Chronic Obstructive Pulmonary Disease Federated Learning Adaptive Transformers Ensemble Learning Spatial-Temporal Features Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 03 Oct, 2025 Editor invited by journal 03 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 02 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. 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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-7455494","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":603301178,"identity":"1cac6e54-c3ea-4407-86b4-74559b6e922c","order_by":0,"name":"Ayesha Jabbar","email":"","orcid":"","institution":"College of Electronics and Information Engineering, chShenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Jabbar","suffix":""},{"id":603301181,"identity":"934ee8ca-5dc7-4beb-ad5b-f08ada9af02a","order_by":1,"name":"Huang Jianjun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACAzDJI8HAD+Ezk6BFsoE0LSDGAWK1mEskP3v4RcYiz/j88WcSDBXWiQ3sZw/g1WI5I83cWIZHotjsRkKaBMOZ9MQGnrwE/A67kWAmLcEjkbjtBsMxCca2w4kNEjwGBLSkfwNr2dx/sE2C8R9RWnLMJD8AtWxgSGaTYGwgRsuZN2XSwEBOnHEjjdki4Vi6cRtPDgEtx9O3Sf7sqUvs7z/+8MaHGmvZfvYz+LWAADNvD5SVAMRsBNUDAeOPH8QoGwWjYBSMghELAHSqQRNUFrYbAAAAAElFTkSuQmCC","orcid":"","institution":"College of Electronics and Information Engineering, chShenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Huang","middleName":"","lastName":"Jianjun","suffix":""},{"id":603301182,"identity":"9759c383-fd38-42e3-89b4-7defcb0ea602","order_by":2,"name":"Muhammad Kashif Jabbar","email":"","orcid":"","institution":"College of Electronics and Information Engineering, chShenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Kashif","lastName":"Jabbar","suffix":""},{"id":603301183,"identity":"898f7c43-2a81-49c0-b2ec-2f7d5ee3e4a4","order_by":3,"name":"Asad Ali","email":"","orcid":"","institution":"College of Electronics and Information Engineering, chShenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Asad","middleName":"","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2025-08-25 16:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7455494/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7455494/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780890,"identity":"a7a90d18-f93a-4760-8ac7-4a29af49518d","added_by":"auto","created_at":"2026-03-17 07:54:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2794228,"visible":true,"origin":"","legend":"","description":"","filename":"UpdatedManuscriptFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7455494/v1_covered_f511ea46-048d-4b3d-9839-7323c5d38bd8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Preclinical Detection and Progression Prediction of Neurodegenerative Diseases Using Multi-Modal Deep Learning","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":"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},"keywords":"Chronic Obstructive Pulmonary Disease, Federated Learning, Adaptive Transformers, Ensemble Learning, Spatial-Temporal Features","lastPublishedDoi":"10.21203/rs.3.rs-7455494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7455494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) continue to pose significant global health challenges, particularly due to their insidious progression and the critical need for early, preclinical diagnosis. 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