Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/4294
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Roland, Gilbert | en_US |
dc.contributor.author | Kumar, Navin | en_US |
dc.contributor.author | Gururaj, Bharathi | en_US |
dc.contributor.author | Richa, Richa | en_US |
dc.contributor.author | Bobade, Sunil Devidas | en_US |
dc.contributor.author | Lourens, Melanie Elizabeth | en_US |
dc.date.accessioned | 2022-09-29T07:17:45Z | - |
dc.date.available | 2022-09-29T07:17:45Z | - |
dc.date.issued | 2022-07-01 | - |
dc.identifier.citation | Roland, 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.v6ns1.8606 | en_US |
dc.identifier.issn | 2550-6978 | - |
dc.identifier.issn | 2550-696X (Online) | - |
dc.identifier.uri | https://hdl.handle.net/10321/4294 | - |
dc.description.abstract | In 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.extent | 15 p. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universidad Tecnica de Manabi | en_US |
dc.relation.ispartof | International Journal of Health Sciences; Vol. Special Issue 1 | en_US |
dc.subject | Diabetes mellitus | en_US |
dc.subject | Neural network | en_US |
dc.subject | Artificial intelligence | en_US |
dc.title | Artificial intelligence–based neural network for the diagnosis of diabetes and COVID | en_US |
dc.type | Article | en_US |
dc.date.updated | 2022-09-05T10:25:35Z | - |
dcterms.dateAccepted | 2022-3-1 | - |
dc.identifier.doi | 10.53730/ijhs.v6ns1.8606 | - |
local.sdg | SDG03 | en_US |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.grantfulltext | restricted | - |
Appears in Collections: | Theses and dissertations (Management Sciences) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Gilbert_G et al_2022.pdf | Article | 867.19 kB | Adobe PDF | View/Open |
IJHS Copyright Clearance.docx | 169.11 kB | Microsoft Word XML | View/Open |
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