Predicting Amyotrophic Lateral Sclerosis Progression Using Optimized Deep Learning Architectures

Predicting Amyotrophic Lateral Sclerosis Progression Using Optimized Deep Learning Architectures | 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 Predicting Amyotrophic Lateral Sclerosis Progression Using Optimized Deep Learning Architectures Ariej Al-Bdairat, Salwani Abdullah, Sofian Kassaymeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7197090/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 Amyotrophic Lateral Sclerosis (ALS) is a progressive and fatal neurodegenerative disorder characterized by the deterioration of motor neurons, leading to muscle weakness, paralysis, and ultimately respiratory failure. Accurate prediction of ALS progression is critical for timely clinical decision-making, personalized patient management, and optimizing clinical trials. However, the heterogeneity of disease trajectories and limited temporal clinical observations pose significant challenges in building robust predictive models. To address these limitations, this study proposes a deep learning-based framework utilizing both Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) models to forecast ALS progression using the PRO-ACT dataset. The proposed architecture incorporates a multi-stage optimization framework designed to enhance model accuracy and generalization. Two nature-inspired metaheuristic algorithms—the Snake Optimizer and the Dung Beetle Optimizer—are independently applied to fine-tune key hyperparameters. Then, a novel hybrid optimizer is introduced, which sequentially combines the global search capability of the Snake algorithm with the local refinement strengths of the Dung Beetle algorithm. In parallel, Optuna—a Bayesian optimization framework—is employed as a baseline for automated hyperparameter tuning. The model also leverages Pearson correlation analysis for feature selection and standardizes inputs to ensure consistency and convergence. Experimental results show the LSTM model optimized with the Snake algorithm achieved a minimum validation Mean Squared Error (MSE) of 0.3146. The hybrid optimizer demonstrated robust convergence with an MSE of 0.3146 for LSTM and 0.3157 for DNN. Optuna-based models achieved MSEs of 0.3198 for LSTM and 0.3161 for DNN. These results highlight the effectiveness of AI-driven hybrid optimization in predicting ALS progression and supporting clinical intervention strategies. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Neuroscience 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. 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-7197090","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":500782293,"identity":"4b262d19-1247-42fc-b450-966b60af81d0","order_by":0,"name":"Ariej Al-Bdairat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACe4YEhgNgFnsDAzNRWgwbwFoMGBh4DhCpxeBAAphiYJBIINaW9uSHhytq/jDwz3xj+LmgwoaBv707Aa8We55nBgfPHDNgkLidYyw940wag8SZsxvw2zIjweBgAxvQYbdzDKR52w4zGEjk4tdicCP9w8GGfwYM8jfPGP8mUkuOwcHGNgMgg8eMOFsMe94UHGzsM2YwPJNWZs1zJo2HoF/s2dM3f2z4Jscgd/zw5ts8FTZy/O29+LXAQH0DA4cBiMFDlHIoYH9AiupRMApGwSgYQQAAOlNJovAT4DQAAAAASUVORK5CYII=","orcid":"","institution":"National University of Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Ariej","middleName":"","lastName":"Al-Bdairat","suffix":""},{"id":500782294,"identity":"8e6e4d68-7e02-40fe-af4d-03d5b16182b0","order_by":1,"name":"Salwani Abdullah","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Salwani","middleName":"","lastName":"Abdullah","suffix":""},{"id":500782295,"identity":"aefc785c-8a3e-406f-ba10-60493f74512e","order_by":2,"name":"Sofian Kassaymeh","email":"","orcid":"","institution":"Aqaba University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sofian","middleName":"","lastName":"Kassaymeh","suffix":""}],"badges":[],"createdAt":"2025-07-23 13:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7197090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7197090/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90533938,"identity":"f198e46f-9ee5-4009-9b7f-cef7f58e1cb4","added_by":"auto","created_at":"2025-09-03 19:16:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1263159,"visible":true,"origin":"","legend":"","description":"","filename":"PredictingAmyotrophicLateralSclerosisProgressionUsingOptimizedDeepLearningmodels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7197090/v1_covered_653e5070-f3a9-45bd-b4bc-f85fbca2a5fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Amyotrophic Lateral Sclerosis Progression Using Optimized Deep Learning Architectures","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7197090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7197090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Amyotrophic Lateral Sclerosis (ALS) is a progressive and fatal neurodegenerative disorder characterized by the deterioration of motor neurons, leading to muscle weakness, paralysis, and ultimately respiratory failure. Accurate prediction of ALS progression is critical for timely clinical decision-making, personalized patient management, and optimizing clinical trials. However, the heterogeneity of disease trajectories and limited temporal clinical observations pose significant challenges in building robust predictive models. To address these limitations, this study proposes a deep learning-based framework utilizing both Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) models to forecast ALS progression using the PRO-ACT dataset. The proposed architecture incorporates a multi-stage optimization framework designed to enhance model accuracy and generalization. Two nature-inspired metaheuristic algorithms—the Snake Optimizer and the Dung Beetle Optimizer—are independently applied to fine-tune key hyperparameters. Then, a novel hybrid optimizer is introduced, which sequentially combines the global search capability of the Snake algorithm with the local refinement strengths of the Dung Beetle algorithm. In parallel, Optuna—a Bayesian optimization framework—is employed as a baseline for automated hyperparameter tuning. The model also leverages Pearson correlation analysis for feature selection and standardizes inputs to ensure consistency and convergence. Experimental results show the LSTM model optimized with the Snake algorithm achieved a minimum validation Mean Squared Error (MSE) of 0.3146. The hybrid optimizer demonstrated robust convergence with an MSE of 0.3146 for LSTM and 0.3157 for DNN. Optuna-based models achieved MSEs of 0.3198 for LSTM and 0.3161 for DNN. 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