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

Abstract

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