Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/4294
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dc.contributor.authorRoland, Gilberten_US
dc.contributor.authorKumar, Navinen_US
dc.contributor.authorGururaj, Bharathien_US
dc.contributor.authorRicha, Richaen_US
dc.contributor.authorBobade, Sunil Devidasen_US
dc.contributor.authorLourens, Melanie Elizabethen_US
dc.date.accessioned2022-09-29T07:17:45Z-
dc.date.available2022-09-29T07:17:45Z-
dc.date.issued2022-07-01-
dc.identifier.citationRoland, G. et al. 2022. Artificial intelligence–based neural network for the diagnosis of diabetes and COVID. International journal of health sciences. Special Issue 1: 13945-13959. doi:10.53730/ijhs.v6ns1.8606en_US
dc.identifier.issn2550-6978-
dc.identifier.issn2550-696X (Online)-
dc.identifier.urihttps://hdl.handle.net/10321/4294-
dc.description.abstractIn many nations, the prevalence of diabetes is rising, and its impact on national health cannot be overlooked. Smart medicine is a medical concept in which technology is used to aid in disease detection and treatment. The objective of this study is to take a gander at the information and look at changed diabetic mellitus forecasting algorithms. According to rising dismalness as of late, the quantity of diabetic patients worldwide will arrive at 642 million out of 2040, suggesting that one out of each 10persons would be affected. This worrisome figure, without a question, demands immediate attention. AI has been applied to an assortment of aspects of clinical wellbeing as a result of its rapid progress. To predict diabetes mellitus in this review, we utilized a choice tree, an arbitrary timberland, and a neural organization.en_US
dc.format.extent15 p.en_US
dc.language.isoenen_US
dc.publisherUniversidad Tecnica de Manabien_US
dc.relation.ispartofInternational Journal of Health Sciences; Vol. Special Issue 1en_US
dc.subjectDiabetes mellitusen_US
dc.subjectNeural networken_US
dc.subjectArtificial intelligenceen_US
dc.titleArtificial intelligence–based neural network for the diagnosis of diabetes and COVIDen_US
dc.typeArticleen_US
dc.date.updated2022-09-05T10:25:35Z-
dcterms.dateAccepted2022-3-1-
dc.identifier.doi10.53730/ijhs.v6ns1.8606-
local.sdgSDG03en_US
item.openairetypeArticle-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
Appears in Collections:Theses and dissertations (Management Sciences)
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