Machine Learning Validation of Numerical Method Based Computational Analysis of Cysts and Malignant Tumors in Breast

Machine Learning Validation of Numerical Method Based Computational Analysis of Cysts and Malignant Tumors in Breast | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Machine Learning Validation of Numerical Method Based Computational Analysis of Cysts and Malignant Tumors in Breast Rabia Musheer Aziz, Akshara Makrariya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1586801/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 Tumor is an abnormal tissue which can be appeared at any part of the body. It can be classified to either benign or malignant. One of the most common women's tumors that infest the breast. Various benign disorders like development of cysts in woman’s breast occur due to hormonal changes and are at the risk of becoming malignant. A number of thermal models are reported to differentiate between normal and malignant tissues of breast. But no thermal model is reported in study the effect of benign disorders on the literature to distinguish between benign and malignant disorders in woman’s breast. An attempt has been made in this paper to study the thermal disturbances caused by cysts and malignant tumors in the fat tissues of woman’s breast. The model is developed for a two-dimensional steady state case using penne’s bio heat equation and incorporating parameters like thermal conductivity, blood mass flow rate and self-controlled metabolic heat generation. The appropriate adiabatic boundary conditions have been framed for various environmental conditions. The finite element method has been employed to obtain the solution. The results have been obtained for different sizes of spherical shaped cysts and different depth of tissues in hemispherical shaped woman’s breast. The relation of size and position of the cysts have been studied with the thermal distribution in various tissues layers of the woman’s breast. The comparison of thermal profiles for cysts and malignant tumors in woman’s breast has been performed. A contrast in thermal behavior of cyst and malignant tumor in woman’s breast is observed which can be useful to distinguish between the malignant tumor and cyst in woman’s breast to prevent false positive test for malignant tumor. Accordingly, this study found that there are various factors that could affect the cancer classification and prediction. Therefore in this study, Breast cancer data classification have been done using three classification techniques which are Artifical Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) in order to improve the performance of the model trained the model with selected features according to the analysis done. Benign Disorders Finite Element method Thermal Disturbances Artifical Neural Network (ANN) Support Vector Machine (SVM) Random Forest (RF) Full Text Supplementary Files GraphicalAbstract.docx 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 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-1586801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":104408835,"identity":"ac43d6cd-19bc-4f85-9c2d-92b9ebaa0f0a","order_by":0,"name":"Rabia Musheer Aziz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3PMUvDQBTA8XccvOkwa4aCX+FEOJeQfBCXC4G4tM4BC8alXQr9JK7OLxzUJZq14HLdi9Stg4PXiBSEJLoJ3n+5N7wfjwPw+f5kAujwIHAiLSM3sjv6IcHU2iI/kHKQfA3nZ7Y27dhLLuZPFe0hTk4CUmE6a+L7uXFXptFlFxnV17paQMYRKHfkJXuoU0dW+aTsICGMJQngiKxctUSRI6w03STYyuodbgVyNnPkOVPNZoCEY2kEmBCRc6lritV66Er4qs1IPkoUyKwuMq3W7oru+0swMW/b4iY5XTa7ai/jRDVXG7ubRp3kM3kc03ZT965/K/nNss/n8/2PPgCyNWDJO4wk0QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2655-7272","institution":"VIT Bhopal University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rabia","middleName":"Musheer","lastName":"Aziz","suffix":""},{"id":104408836,"identity":"de5d3389-80f4-4477-bada-04c2f6fe5e38","order_by":1,"name":"Akshara Makrariya","email":"","orcid":"","institution":"VIT Bhopal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Akshara","middleName":"","lastName":"Makrariya","suffix":""}],"badges":[],"createdAt":"2022-04-23 07:49:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1586801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1586801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21333348,"identity":"05706815-938e-42f9-97fd-63daf736dac9","added_by":"auto","created_at":"2022-05-11 12:53:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":789374,"visible":true,"origin":"","legend":"","description":"","filename":"Finalmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1586801/v1_covered.pdf"},{"id":21333347,"identity":"df2ae098-f53d-4c95-b343-a5664dc18aaf","added_by":"auto","created_at":"2022-05-11 12:53:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":255888,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-1586801/v1/336042104c785527adc457ec.docx"}],"financialInterests":"","formattedTitle":"Machine Learning Validation of Numerical Method Based Computational Analysis of Cysts and Malignant Tumors in Breast","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1586801/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"Benign Disorders, Finite Element method, Thermal Disturbances, Artifical Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF)","lastPublishedDoi":"10.21203/rs.3.rs-1586801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1586801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTumor is an abnormal tissue which can be appeared at any part of the body. It can be classified to either benign or malignant. One of the most common women's tumors that infest the breast. Various benign disorders like development of cysts in woman\u0026rsquo;s breast occur due to hormonal changes and are at the risk of becoming malignant. A number of thermal models are reported to differentiate between normal and malignant tissues of breast. But no thermal model is reported in study the effect of benign disorders on the literature to distinguish between benign and malignant disorders in woman\u0026rsquo;s breast. An attempt has been made in this paper to study the thermal disturbances caused by cysts and malignant tumors in the fat tissues of woman\u0026rsquo;s breast. The model is developed for a two-dimensional steady state case using penne\u0026rsquo;s bio heat equation and incorporating parameters like thermal conductivity, blood mass flow rate and self-controlled metabolic heat generation. The appropriate adiabatic boundary conditions have been framed for various environmental conditions. The finite element method has been employed to obtain the solution. The results have been obtained for different sizes of spherical shaped cysts and different depth of tissues in hemispherical shaped woman\u0026rsquo;s breast. The relation of size and position of the cysts have been studied with the thermal distribution in various tissues layers of the woman\u0026rsquo;s breast. The comparison of thermal profiles for cysts and malignant tumors in woman\u0026rsquo;s breast has been performed. A contrast in thermal behavior of cyst and malignant tumor in woman\u0026rsquo;s breast is observed which can be useful to distinguish between the malignant tumor and cyst in woman\u0026rsquo;s breast to prevent false positive test for malignant tumor. Accordingly, this study found that there are various factors that could affect the cancer classification and prediction. Therefore in this study, Breast cancer data classification have been done using three classification techniques which are Artifical Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) in order to improve the performance of the model trained the model with selected features according to the analysis done.\u003c/p\u003e","manuscriptTitle":"Machine Learning Validation of Numerical Method Based Computational Analysis of Cysts and Malignant Tumors in Breast","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-11 12:53:17","doi":"10.21203/rs.3.rs-1586801/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":"2c134c26-84b9-4b7a-be9d-6fd4924cee06","owner":[],"postedDate":"May 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-05-11T12:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-11 12:53:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1586801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1586801","identity":"rs-1586801","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}