Predicting Quality of Life and Mental Health During the First Year After Trauma Using Radial Basis Function Neural Network 

Predicting Quality of Life and Mental Health During the First Year After Trauma Using Radial Basis Function Neural Network  | 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 Predicting Quality of Life and Mental Health During the First Year After Trauma Using Radial Basis Function Neural Network Zahra Sehat, Esmaeil Fakharian, Mojtaba Sehat, Abdollah Omidi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-62812/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 Background: This study aimed to investigate the quality of life (QOL) and mental health (MH) post-trauma in the population over 15 years. Methods: A cross-sectional population-based study, the cluster sampling method was used and 3880 people were interviewed randomly selected individuals between over 15 years in each household in the city of Kashan. Data were analyzed using chi-square, t-test. After data collection, a radial basis function neural network (RBFNN) architecture is exploited to predict the QOL category and MH status after traumatic injuries. Results : The rate of trauma was 70.64 (62.60-78.70) in 1000 annually, and 77.73% were male. 38.3% of people with trauma have suspected of having mental disorder and 53.3% of people with injury were in good condition of QOL. The risk of suspected MH disorders in people with trauma during the last year was 1.2(0.96-1.61), and the risk of bad QOL was 2.6(1.8-3.7). The obtained results reveal that the RBFNN model can predict the QOL category and MH status with a high level of accuracy (maximum 1% predicting error). Conclusion :This study reveals several parameters associated with the MH and QOL after trauma. This parameter can be used in prediction outcomes and used to evaluate the care of people with injury. It can also be concluded that the RBFNN predictor can be used for predicting the QOL category and MH status with reasonable authenticity, efficiency, and accuracy. Health Economics & Outcomes Research Epidemiology mental health prediction radial basis function neural network quality of life trauma Figures Figure 1 Figure 2 Figure 3 Figure 4 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. 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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-62812","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":1770135,"identity":"282c32b7-28ab-4033-9c23-c91b363ccfa2","order_by":0,"name":"Zahra 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PDF\u003c/a\u003e.\u003c/p\u003e"}],"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":true,"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":"Epidemiology, mental health, prediction, radial basis function neural network, quality of life, trauma","lastPublishedDoi":"10.21203/rs.3.rs-62812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-62812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\t\u003cstrong\u003eBackground: \u003c/strong\u003eThis study aimed to investigate the quality of life (QOL) and mental health (MH) post-trauma in the population over 15 years. \u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional population-based study,\u0026nbsp;the cluster sampling method was used and 3880 people were interviewed randomly selected individuals between over 15 years in each household in the city of Kashan. Data were analyzed using chi-square, t-test. After data collection, a radial basis function neural network (RBFNN) architecture is exploited to predict the QOL category and MH status after traumatic injuries.\u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eResults :\u003c/strong\u003eThe rate of trauma was 70.64 (62.60-78.70) in 1000 annually, and 77.73% were male. 38.3% of people with trauma have suspected of having mental disorder and 53.3% of people with injury were in good condition of QOL. The risk of suspected MH disorders in people with trauma during the last year was 1.2(0.96-1.61), and the risk of bad QOL was 2.6(1.8-3.7). The obtained results reveal that the RBFNN model can predict the QOL category and MH status with a high level of accuracy (maximum 1% predicting error). \u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eConclusion \u003c/strong\u003e:This study reveals several parameters associated with the MH and QOL after trauma. This parameter can be used in prediction outcomes and used to evaluate the care of people with injury. It can also be concluded that the RBFNN predictor can be used for predicting the QOL category and MH status with reasonable authenticity, efficiency, and accuracy.\u003c/p\u003e","manuscriptTitle":"Predicting Quality of Life and Mental Health During the First Year After Trauma Using Radial Basis Function Neural Network\u0026nbsp;","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-28 17:19:49","doi":"10.21203/rs.3.rs-62812/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":"f8c69792-caba-4549-a5b1-75fa7a6957fd","owner":[],"postedDate":"August 28th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":385644,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2021-06-03T08:01:56+00:00","versionOfRecord":[],"versionCreatedAt":"2020-08-28 17:19:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-62812","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-62812","identity":"rs-62812","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}