Impact Analysis of Natural Herb on Streptozotocin-Induced Diabetic Rats using Machine Learning Method

Impact Analysis of Natural Herb on Streptozotocin-Induced Diabetic Rats using Machine Learning Method | 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 Impact Analysis of Natural Herb on Streptozotocin-Induced Diabetic Rats using Machine Learning Method Dr. Paramita Ray, Dr. Susmita This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4541434/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 Diabetes mellitus (DM) is a chronic metabolic disorder affecting millions globally. Effective management of DM is crucial to mitigate its complications. Natural herbs, including turmeric (Curcuma longa), aloe vera, and basil (Ocimum basilicum), are known for their medicinal properties and have been explored for their potential benefits in diabetes management. Objective of the paper is to evaluate the impact of the herbal treatment on Fasting Blood Glucose (FBG), Urea, Creatinine levels and Blood Urea Nitrogen (BUN) levels and employ machine learning models to predict and analyze the effectiveness of different doses of the herbal treatment. In this study, we divided our work into two phases. Firstly, we performed the experiment with adult Wistar albino rats and rats were made diabetic through intraperitoneal injection of freshly prepared streptozotocin (STZ). The diabetic rats were split into an untreated control group and treated test groups. The treated groups received varying doses of an aqueous solution of natural herbs (turmeric, aloe vera, and basil) administered orally through gavage for 21 days. In second phase, experimental data were collected and analyzed to detect the impact of natural herbs on diabetic rats. The paper proposes an algorithm that integrates the Bayesian Structural Time Series (BSTS) model with the Dynamic Time Warping (DTW) method. The Bayesian Structural Time Series (BSTS) model has been used as a baseline to determine the impact of natural herbs on specific health metrics such as Fasting Blood Glucose (FBG), Urea, Creatinine, and Blood Urea Nitrogen (BUN) levels. The DTW algorithm has been used in conjunction with the BSTS model to enhance performance. This combination of BSTS and DTW aims to provide a more comprehensive analysis of the effects of natural herbs on the health metrics of diabetic rats, highlighting the potential therapeutic benefits of these herbs in managing diabetes. Significant reduction (p=0.001) in FBG levels, Urea, Creatinine and BUN levels indicates the potential of this herbal combination in managing blood sugar levels. These findings suggest that the combined herbal preparation has a beneficial effect on the treatment of diabetes mellitus. Bioinformatics Diabetes Mellitus Streptozotocin Bayesian Structural time series model Impact analysis turmeric or Curcuma longa and basil or Ocimum basilicum Dynamic Time Warping Full Text Additional Declarations The authors declare no competing interests. Supplementary Files caption.sty 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. 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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-4541434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311434032,"identity":"23fdfa8c-1cd6-4e6f-8d6b-f9536302aa64","order_by":0,"name":"Dr. Paramita Ray","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDADNgYGxgcMDAdI0cLGwGxAmhagNWwSRGkxbz9j9uDnHrtoPvnmZ9U8NXfk+BmYHz66gUeLzJkcc8OeZ8m5bWxsZrd5jj0zlmxgMzbOwaNFgiHHTILnADNQCwNQC9vhxA0HeNik8Wrhf2Mm+edAPVAL+7dinn/EaJHIMZPmOXAYqIXHjJm3jSgtz8qNZQ4cB2rJKZac23fYWLKZkF/4k7c9fHOgOnd+8/GNH958OyzHz9788DE+LQwMHGZwJhMPiGTGqxwE2J/BmYw/CKoeBaNgFIyCkQgAgORJQMoSvuoAAAAASUVORK5CYII=","orcid":"","institution":"Dinabandhu Andrews institute of Technology and Management","correspondingAuthor":true,"prefix":"Dr.","firstName":"Paramita","middleName":"","lastName":"Ray","suffix":""},{"id":311434033,"identity":"672d4f34-0ac4-47c3-9ea4-131a272ab664","order_by":1,"name":"Dr. Susmita","email":"","orcid":"","institution":"Roy","correspondingAuthor":false,"prefix":"Dr.","firstName":"","middleName":"","lastName":"Susmita","suffix":""}],"badges":[],"createdAt":"2024-06-06 15:30:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4541434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4541434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57883745,"identity":"044dcfe3-0559-4c2f-9abc-955e35f9cb25","added_by":"auto","created_at":"2024-06-07 03:48:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":622925,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4541434/v1_covered_78260348-fd7f-4739-b8a2-4a5b35f5d7e2.pdf"},{"id":57883742,"identity":"58969ba4-8675-4a8f-8373-34b38ebe37a6","added_by":"auto","created_at":"2024-06-07 03:48:08","extension":"sty","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4731,"visible":true,"origin":"","legend":"","description":"","filename":"caption.sty","url":"https://assets-eu.researchsquare.com/files/rs-4541434/v1/a3528a3e4aa129d88899412c.sty"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImpact Analysis of Natural Herb on Streptozotocin-Induced Diabetic Rats using Machine Learning Method\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Calcutta","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":"Diabetes Mellitus, Streptozotocin, Bayesian Structural time series model, Impact analysis, turmeric or Curcuma longa and basil or Ocimum basilicum, Dynamic Time Warping","lastPublishedDoi":"10.21203/rs.3.rs-4541434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4541434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetes mellitus (DM) is a chronic metabolic disorder affecting millions globally. Effective management of DM is crucial to mitigate its complications. Natural herbs, including turmeric (Curcuma longa), aloe vera, and basil (Ocimum basilicum), are known for their medicinal properties and have been explored for their potential benefits in diabetes management. Objective of the paper is to evaluate the impact of the herbal treatment on Fasting Blood Glucose (FBG), Urea, Creatinine levels and Blood Urea Nitrogen (BUN) levels and employ machine learning models to predict and analyze the effectiveness of different doses of the herbal treatment. In this study, we divided our work into two phases. Firstly, we performed the experiment with adult Wistar albino rats and rats were made diabetic through intraperitoneal injection of freshly prepared streptozotocin (STZ). The diabetic rats were split into an untreated control group and treated test groups. The treated groups received varying doses of an aqueous solution of natural herbs (turmeric, aloe vera, and basil) administered orally through gavage for 21 days. In second phase, experimental data were collected and analyzed to detect the impact of natural herbs on diabetic rats. The paper proposes an algorithm that integrates the Bayesian Structural Time Series (BSTS) model with the Dynamic Time Warping (DTW) method. The Bayesian Structural Time Series (BSTS) model has been used as a baseline to determine the impact of natural herbs on specific health metrics such as Fasting Blood Glucose (FBG), Urea, Creatinine, and Blood Urea Nitrogen (BUN) levels. The DTW algorithm has been used in conjunction with the BSTS model to enhance performance. This combination of BSTS and DTW aims to provide a more comprehensive analysis of the effects of natural herbs on the health metrics of diabetic rats, highlighting the potential therapeutic benefits of these herbs in managing diabetes. 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