Evaluating the Accuracy of Obesity Classification with Tree-Based Models: Decision Trees, Random Forests, and Gradient Boosting
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Abstract
Obesity is a condition of excess weight that can have a negative impact on health. This condition increases the risk of various diseases, such as heart disease, type 2 diabetes, hypertension, and other metabolic disorders. According to the World Health Organization (WHO) report, in 2022 there will be 2.5 billion adults aged 18 years and over who are overweight, including more than 890 million people who are obese. Seeing this problem, this study aims to develop an obesity classification model based on age, gender, body mass index (BMI), physical activity, and obesity category attributes. The models used in this study are decision tree, random forest, and gradient boosting, which are included in tree-based methods. The research stages include data collection, dataset processing, model building, and performance evaluation. In the final stage, the best method was selected from the three models used. The results showed that decision tree has an accuracy of 99.5%, random forest 99.7%, and gradient boosting 99.8%, making it the method with the best accuracy in obesity classification. With these results, gradient boosting can be used as a tool in health decision making, especially in detecting and categorizing individuals at risk of obesity more accurately. In addition, this model can help in developing more effective prevention and intervention strategies in dealing with obesity.
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