Abstract:

Obesity is a health problem that is the fifth most important cause of death worldwide. In the report published in 2022, the World Health Organization (WHO) emphasized that obesity forms the basis of many diseases and can be stopped by following the necessary measures and policies. Therefore, obesity analysis and prediction applications with machine learning algorithms are important. In this study, prediction models were developed with K-Nearest Neighbor Algorithm (KNN) and Random Forest Algorithms (RF), which are supervised learning algorithms, using data from the UCI machine learning data repository. These models were compared using different statistical evaluation criteria. As a result of the evaluation, the RF model with hyperparameter optimization achieved the best prediction result with an average accuracy of 94%. The study is important because it analyzes and visualizes the factors affecting the prevalence of obesity and predicts its levels with a high success rate.

Keywords: