RFFE - Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus.

Usha Ruby, A; George Chellin Chandran, J; Swasthika Jain, T J; Chaithanya, B N; Patil, Renuka

Usha Ruby, A; George Chellin Chandran, J; Swasthika Jain, T J; Chaithanya, B N; Patil, Renuka.

Afiliación

Usha Ruby A; School of Computing Science and Engineering Department, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh-466114, India.
George Chellin Chandran J; School of Computing Science and Engineering Department, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh-466114, India.
Swasthika Jain TJ; Department of Computer Science and Engineering, GITAM School of Technology, Nagadenehalli, Doddaballapura, Karnataka-561203, India.
Chaithanya BN; Department of Computer Science and Engineering, GITAM School of Technology, Nagadenehalli, Doddaballapura, Karnataka-561203, India.
Patil R; Department of Computer Science and Engineering, GITAM School of Technology, Nagadenehalli, Doddaballapura, Karnataka-561203, India.

AIMS Public Health ; 10(2): 422-442, 2023.

Article en En | MEDLINE | ID: mdl-37304588

Palabras clave

Fuzzy Entropy; Synthetic Gradient Descent Technique; diabetes diseases; machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies Idioma: En Revista: AIMS Public Health Año: 2023 Tipo del documento: Article País de afiliación: India Pais de publicación: Estados Unidos

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