Obesity classification: a comparative study of machine learning models excluding weight and height data

Machine learning methods have the potential to be used more extensively in the classification of obesity and in more effective efforts to combat obesity.

Authors

Erkut Arıcan (ORCID: https://orcid.org/0000-0003-4528-3203)
Ahmed Cihad Genç

Institutions

Sakarya University (TR)
Bahçeşehir University (TR)

Publication Details

Journal: Revista da Associação Médica Brasileira
Published: 2025-01-01
DOI: https://doi.org/10.1590/1806-9282.20241282
Citations: 5
Primary Topic: Nutritional Studies and Diet
Type: article
Field-Weighted Citation Impact: 9.39

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article

Obesity classification: a comparative study of machine learning models excluding weight and height data

Erkut Arıcan, Ahmed Cihad Genç

Jan 1, 2025

5 citations

Revista da Associação Médica Brasileira

Nutritional Studies and Diet

9.39

article

Obesity classification: a comparative study of machine learning models excluding weight and height data

Erkut Arıcan, Ahmed Cihad Genç

January 1, 2025article en

5 citations

Abstract

Machine learning methods have the potential to be used more extensively in the classification of obesity and in more effective efforts to combat obesity.

Revista da Associação Médica BrasileiraVol. 71(1)

Sakarya University (TR), Bahçeşehir University (TR)

Openalex Percentile: Top 2%

Nutritional Studies and Diet

9.39

DOI Open Access PDF OpenAlex

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Obesity classification: a comparative study of machine learning models excluding weight and height data — Erkut Arıcan, Ahmed Cihad Genç · Revista da Associação Médica Brasileira (2025) | TGRS Research Map | TGRS