Soft Computing Framework for State-Level Prediction of Biomedical Waste Generation in India: An ANN-Based Multi-Factor Analysis

Soft Computing Framework for State-Level Prediction of Biomedical Waste Generation in India: An ANN-Based Multi-Factor 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 Research Article Soft Computing Framework for State-Level Prediction of Biomedical Waste Generation in India: An ANN-Based Multi-Factor Analysis Usman Usman Aliyu, Sukalpaa Chaki, Tushar Bansal, Rakesh Kumar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7164362/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 This paper explores the application of an artificial neural network (ANN) to forecast annual biomedical waste (BMW) production at the state level in India. Traditional methods are inadequate for precise prediction due to the varied sources of BMW, and advanced techniques are required to overcome such. The study employed a feed-forward neural network with sigmoid hidden and output neurons to create a model for the estimation. At the same time, Levenberg-Marquardt backpropagation was used to optimize the network. Various health, social demographic, and economic factors were used as input variables, and ten models were developed using sensitivity analysis. An annual dataset covering the years 2010–2020 was used to train the algorithms. This scenario marks the first application of BMW forecasting using whole Indian statistics, providing a notable advancement in the field. The statistics showed a significant increase in BMW generation, from 410.33 tons/day in 2011 to 628.3 tons/day in 2020, highlighting the urgency of predictive models in consideration of the growing population and waste yield. Key variables analyzed include hospital beds, state gross domestic product trends, healthcare facilities (HCFs), urban population, authorized HCFs, the number of operational common BMW treatment facilities, and HCF authorization applications. Models' performance was evaluated using mean square error (MSE) and determination coefficient (R), with model seven exhibiting the best performance with an MSE of 0.0032 and R of 0.975. The study concludes that the ANN approach can effectively predict future BMW production in India and other cities facing similar issues for adequate planning and improvement approaches in the healthcare system and waste management. The findings emphasized the importance of advanced modeling approaches for BMW generation while providing significant and applied implications for policy and environmental management concerns. Biomedical Waste Management Healthcare Facilities Artificial Neural Network (ANN) Feed Forward Neutral Network Modeling Full Text 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-7164362","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505996715,"identity":"74e914ca-f241-45a9-befd-2fa44207c127","order_by":0,"name":"Usman Usman Aliyu","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0008-0041-0233","institution":"Sharda University","correspondingAuthor":true,"prefix":"","firstName":"Usman","middleName":"Usman","lastName":"Aliyu","suffix":""},{"id":505996716,"identity":"c448674c-caec-48f3-9fbe-151205727d7c","order_by":1,"name":"Sukalpaa Chaki","email":"","orcid":"","institution":"Sharda University School of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sukalpaa","middleName":"","lastName":"Chaki","suffix":""},{"id":505996717,"identity":"13c695dd-a2ad-466b-b2d5-8cffd50c78b8","order_by":2,"name":"Tushar Bansal","email":"","orcid":"","institution":"SET: Sharda University School of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tushar","middleName":"","lastName":"Bansal","suffix":""},{"id":505996718,"identity":"506537e7-b253-423a-b1d1-378c382519ad","order_by":3,"name":"Rakesh Kumar","email":"","orcid":"","institution":"Sharda University School of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Kumar","suffix":""},{"id":505996719,"identity":"0d3c7261-3a04-41cd-83f3-20ff81e2e469","order_by":4,"name":"Sani Isa Abba","email":"","orcid":"","institution":"Prince Mohammad Bin Fahd University","correspondingAuthor":false,"prefix":"","firstName":"Sani","middleName":"Isa","lastName":"Abba","suffix":""}],"badges":[],"createdAt":"2025-07-19 12:14:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7164362/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7164362/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92945261,"identity":"2a8de231-3171-4f0a-be84-90107ac75f47","added_by":"auto","created_at":"2025-10-07 12:31:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":784268,"visible":true,"origin":"","legend":"","description":"","filename":"USMANBMWFFNNmanuscriptR8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7164362/v1_covered_6dcfe347-01dd-46ca-9783-c976b7398b02.pdf"}],"financialInterests":"","formattedTitle":"Soft Computing Framework for State-Level Prediction of Biomedical Waste Generation in India: An ANN-Based Multi-Factor Analysis","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":"Biomedical Waste, Management, Healthcare Facilities, Artificial Neural Network (ANN), Feed Forward Neutral Network, Modeling","lastPublishedDoi":"10.21203/rs.3.rs-7164362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7164362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the application of an artificial neural network (ANN) to forecast annual biomedical waste (BMW) production at the state level in India. Traditional methods are inadequate for precise prediction due to the varied sources of BMW, and advanced techniques are required to overcome such. The study employed a feed-forward neural network with sigmoid hidden and output neurons to create a model for the estimation. At the same time, Levenberg-Marquardt backpropagation was used to optimize the network. Various health, social demographic, and economic factors were used as input variables, and ten models were developed using sensitivity analysis. An annual dataset covering the years 2010\u0026ndash;2020 was used to train the algorithms. This scenario marks the first application of BMW forecasting using whole Indian statistics, providing a notable advancement in the field. The statistics showed a significant increase in BMW generation, from 410.33 tons/day in 2011 to 628.3 tons/day in 2020, highlighting the urgency of predictive models in consideration of the growing population and waste yield. Key variables analyzed include hospital beds, state gross domestic product trends, healthcare facilities (HCFs), urban population, authorized HCFs, the number of operational common BMW treatment facilities, and HCF authorization applications. Models' performance was evaluated using mean square error (MSE) and determination coefficient (R), with model seven exhibiting the best performance with an MSE of 0.0032 and R of 0.975. The study concludes that the ANN approach can effectively predict future BMW production in India and other cities facing similar issues for adequate planning and improvement approaches in the healthcare system and waste management. The findings emphasized the importance of advanced modeling approaches for BMW generation while providing significant and applied implications for policy and environmental management concerns.\u003c/p\u003e","manuscriptTitle":"Soft Computing Framework for State-Level Prediction of Biomedical Waste Generation in India: An ANN-Based Multi-Factor Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 18:40:16","doi":"10.21203/rs.3.rs-7164362/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":"5a1f15d4-d2af-4713-9062-b3808c09c61e","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T05:09:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 18:40:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7164362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7164362","identity":"rs-7164362","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}