A Balanced Machine Learning Approach to Obesity Risk Classification: Comparative Analysis and Feature Importance

Abstract

Obesity is a growing public health concern, particularly among university students who are exposed to lifestyle changes, disordered eating habits, and reduced physical activity. The aim of this study is to classify obesity risk levels among university students using machine learning classification methods and to identify the most influential factors associated with this risk. The study sample consisted of data collected from 445 students studying at Çankırı Karatekin University. In this context, eight machine learning algorithms—Logistic Regression, Random Forest, Extra Trees, Support Vector Machines, K-Nearest Neighbor, Quadratic Discriminant Analysis, Naive Bayes, and Multilayer Perceptron—were compared to classify obesity risk. Class imbalance in the dataset was addressed using the Adaptive Synthetic Sampling (ADASYN) method applied exclusively to the training set. The models were evaluated using standard performance metrics, and the highest accuracy rate (96.26%) was achieved by the Random Forest model, followed by Logistic Regression with 94.77% accuracy. Variable importance analysis indicated that age, internet use scale score, and fast-food consumption frequency were the most influential factors in classification, while the low correlation between variables (|r| < 0.2) suggested that model performance was driven by the combined contribution of multiple features. Overall, the findings demonstrate that the balanced machine learning approach, particularly ensemble-based methods, can classify obesity risk with high accuracy and provide valuable insights for targeted prevention strategies among university students.

Keywords

• Adaptive synthetic sampling
• machine learning
• obesity
• young adults.

Kaynakça

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