Improving the Accuracy of Obesity Classification Using a Stacking Classifier on Imbalanced Data with SMOTE - Sari, Sifa; Soeleman, M.Arief; Maida, Mamay; Novitasari, Hestiana Putri (Journal of Applied Informatics and Computing, 2026)
Overweight continues to be a prevalent public health problem related to lifestyle behavior, eating behaviour and physical activity. The aim of this work is to develop a generalized and robust machine learning model having a high accuracy for categorizing obesity-level. The study applies to the Obesity Dataset with 1610 members and some preprocessing methods such selected data cleaning, categorical attributes transformation, train/test data set split and class imbalance under utilization of SMOTE approach. The modeling process is based on two base learners namely an optimized Random Forest and Gaussian Naïve Bayes that are fused by Stacking Classifier while using Logistic Regression as the meta-model. Experimental results show that the performance of stacking is the best where it obtains an accuracy rate of 86.34%, outperforming each single model. The analysis also reveals enhancements of various classification measures: stacking can indeed model complex non-linear dependencies between instances as well as simple linear ones. In general, the results serve to demonstrate that stacking-based ensemble learning is a strong solution for predicting obesity level and holds promise against early risk detection in preventive health care systems.