Classification of Obesity Levels Using Machine Learning Algorithms

Ahmet Can  CITAK; Sumeyra  SAHIN BAYRAM; Murat KOKLU

doi:10.58190/imiens.2025.157

Authors

Ahmet Can CITAK Technology Faculty, Department of Computer Engineering, Selcuk University, Konya, Türkiye https://orcid.org/0009-0001-1793-1202
Sumeyra SAHIN BAYRAM Faculty of Health Sciences, Department of Nutrition and Dietetics, Selcuk University, Konya, Türkiye https://orcid.org/0000-0003-1413-6294
Murat KOKLU Faculty of Health Sciences, Department of Nutrition and Dietetics, Selcuk University, Konya, Türkiye https://orcid.org/0000-0002-2737-2360

DOI:

https://doi.org/10.58190/imiens.2025.157

Keywords:

Artificial Neural Networks, Classification, Ensemble Learning, Machine Learning, Obesity, Random Forest

Abstract

Obesity has become a critical public health issue on a global scale due to the serious comorbidities and economic burden it brings. The aim of this study is to develop an effective machine learning model that can accurately determine obesity levels based on data including individuals' demographic characteristics and dietary habits, and to compare the performance of tree-based ensemble learning algorithms and Artificial Neural Network (ANN) approaches. In this context, classification was performed using Random Forest, XGBoost, CatBoost, and ANN (Artificial Neural Network) algorithms based on the open-source “Obesity Dataset” obtained from 1,610 participants and containing 14 different attributes. The models' performance was tested using a 5-fold cross-validation method and evaluated based on accuracy, f-score, precision, and recall using a confusion matrix. Experimental results show that tree-based ensemble models outperform the ANN approach in this dataset. The Random Forest algorithm was the most successful model with an accuracy rate of 94.34% and an F-score of 94.36, followed by XGBoost with an accuracy rate of 92.80%. In contrast, YSA remained at an accuracy rate of 82.98% and spent approximately 93 times more time in terms of training duration compared to Random Forest. When considering the obtained outputs, this study demonstrates that ensemble learning methods such as Random Forest are more efficient than ANN models in terms of both prediction accuracy and computational cost in the analysis of tabular health data, and that the developed model can be used as a reliable tool in clinical decision support systems.

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