New AI explained and validated deep learning approaches to accurately predict diabetes.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 63; no. 8; pp. 2373 - 2393 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187120540&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187120540 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2025 vid: 63 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187120540 183430464 10.1007/s11517-025-03338-6 187120540 ppf: 2373 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: New AI explained and validated deep learning approaches to accurately predict diabetes. aug: au: Shaheen, Ifra Javaid, Nadeem Alrajeh, Nabil Asim, Yousra Akber, Syed Muhammad Abrar affil: https://ror.org/04qkq2m54 ComSens Lab, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, 64002, Douliou, Yunlin, Taiwan sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|