Novel Internet of Things based approach toward diabetes prediction using deep learning models.

Naseem, Anum; Habib, Raja; Naz, Tabbasum; Atif, Muhammad; Arif, Muhammad; Allaoua Chelloug, Samia

Naseem, Anum; Habib, Raja; Naz, Tabbasum; Atif, Muhammad; Arif, Muhammad; Allaoua Chelloug, Samia.

Afiliación

Naseem A; Faculty of Computer Sciences, Ibadat International University, Islamabad, Pakistan.
Habib R; Faculty of Computer Sciences, Ibadat International University, Islamabad, Pakistan.
Naz T; CMAC Future Manufacturing Research Hub, University of Strathclyde, Glasgow, United Kingdom.
Atif M; Department of Computer Science and IT, The University of Lahore, Lahore, Pakistan.
Arif M; Department of Computer Science and IT, The University of Lahore, Lahore, Pakistan.
Allaoua Chelloug S; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Front Public Health ; 10: 914106, 2022.

Article en En | MEDLINE | ID: mdl-36091536

Asunto(s)

Aprendizaje Profundo; Diabetes Mellitus; Internet de las Cosas; Diabetes Mellitus/diagnóstico; Humanos; Redes Neurales de la Computación; Máquina de Vectores de Soporte

Palabras clave

CNN; RNN; deep learning; diabetes; machine learning; medical-Internet of Things

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Mellitus / Aprendizaje Profundo / Internet de las Cosas Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Front Public Health Año: 2022 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Suiza

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google