ArticleRevista da Associacao Medica Brasileira (1992)2025
Obesity classification: a comparative study of machine learning models excluding weight and height data.
Article in Revista da Associacao Medica Brasileira (1992), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Mortality Prediction from Patient's First Day PAAC Radiograph in Internal Medicine Intensive Care Unit Using Artificial Intelligence Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis.Scientific reports · 2025Article
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Abstract
objectiveObesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity classification.
methodsWe used a dataset with 2,111 individuals categorized into seven groups based on their body mass index, ranging from average weight to class III obesity. Our classification models were trained and tested using demographic information like age, gender, and eating habits without including height and weight variables.
resultsThe study demonstrated that when trained on demographic information, machine learning can classify body mass index. The random forest model provided the highest performance scores among all the classification models tested in this research.
conclusionMachine learning methods have the potential to be used more extensively in the classification of obesity and in more effective efforts to combat obesity.
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