Genc Ahmed Cihad, Arıcan Erkut
Bahcesehir University, Graduate School, Department of Artificial Intelligence - İstanbul, Türkiye.
Sakarya University, Faculty of Medicine, Department of Internal Medicine - Sakarya, Türkiye.
Rev Assoc Med Bras (1992). 2025 Mar 17;71(1):e20241282. doi: 10.1590/1806-9282.20241282. eCollection 2025.
Obesity 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.
We 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.
The 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.
Machine learning methods have the potential to be used more extensively in the classification of obesity and in more effective efforts to combat obesity.
肥胖是一个全球性的健康问题。本研究旨在分析机器学习模型在预测肥胖类别方面的有效性，并确定哪种模型在肥胖分类中表现最佳。
我们使用了一个包含2111名个体的数据集，这些个体根据其体重指数被分为七组，范围从正常体重到III级肥胖。我们的分类模型使用年龄、性别和饮食习惯等人口统计学信息进行训练和测试，未纳入身高和体重变量。
研究表明，在人口统计学信息上进行训练时，机器学习可以对体重指数进行分类。在本研究测试的所有分类模型中，随机森林模型提供了最高的性能分数。
机器学习方法有潜力在肥胖分类中得到更广泛的应用，并在更有效地对抗肥胖方面发挥作用。
