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...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843688/ |
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| author | Mohammad Azad Md Faraz Kabir Khan Sameh Abd El-Ghany |
| author_facet | Mohammad Azad Md Faraz Kabir Khan Sameh Abd El-Ghany |
| author_sort | Mohammad Azad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-68fc459823514d85bed536ee4e429951 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-68fc459823514d85bed536ee4e4299512025-01-25T00:01:25ZengIEEEIEEE Access2169-35362025-01-0113138471386510.1109/ACCESS.2025.353084010843688XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME ExplanationsMohammad Azad0https://orcid.org/0000-0001-9851-1420Md Faraz Kabir Khan1https://orcid.org/0009-0006-6621-2702Sameh Abd El-Ghany2https://orcid.org/0000-0002-5903-3048Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaDepartment of Computer Science and Software Engineering, The University of Western Australia, Perth, AustraliaDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaObesity 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.https://ieeexplore.ieee.org/document/10843688/Obesitymachine learningstackingexplainable AI |
| spellingShingle | Mohammad Azad Md Faraz Kabir Khan Sameh Abd El-Ghany XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations IEEE Access Obesity machine learning stacking explainable AI |
| title | XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations |
| title_full | XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations |
| title_fullStr | XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations |
| title_full_unstemmed | XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations |
| title_short | XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations |
| title_sort | xai enhanced machine learning for obesity risk classification a stacking approach with lime explanations |
| topic | Obesity machine learning stacking explainable AI |
| url | https://ieeexplore.ieee.org/document/10843688/ |
| work_keys_str_mv | AT mohammadazad xaienhancedmachinelearningforobesityriskclassificationastackingapproachwithlimeexplanations AT mdfarazkabirkhan xaienhancedmachinelearningforobesityriskclassificationastackingapproachwithlimeexplanations AT samehabdelghany xaienhancedmachinelearningforobesityriskclassificationastackingapproachwithlimeexplanations |