XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations

Obesity remains a critical global health challenge, necessitating early risk assessment to
guide preventive measures and mitigate potential complications. While various research endeavors have
explored obesity classification, many existing approaches lack reliability due to limited integration with
explainable artificial intelligence (XAI) methodologies. In this study, we propose a robust machine learning
framework that incorporates Explainable AI (XAI) principles to accurately estimate obesity levels and
provide insights into the factors influencing the predictions. We utilize the publicly available dataset
from Palechor and Manotas available in the UCI ML repository which contains relevant information on
individuals’ physical characteristics and behaviors. Our proposed model employs an ensemble approach,
specifically a stacking algorithm, where the base estimators include the Light Gradient Boosting Machine
(LGBM) classifier, the Logistic Regression (LR) classifier, and the Random Forest (RF) Classifier, and
the Stochastic Gradient Descent (SGD) classifier is selected as the final estimator. To enhance model
interpretability and reliability, we integrate a widely accepted XAI method, Local Interpretable Model-
agnostic Explanations (LIME). Our proposed framework achieves a peak accuracy of 98.82%, surpassing
most existing techniques. By incorporating LIME, we not only enhance model trustworthiness but also
provide deeper insights into the factors driving obesity risk. Overall, our approach contributes to advancing
personalized interventions and bridging the gap between model complexity and human understanding.