Abstract :

Obesity Has Emerged As One Of The Most Critical Public Health Challenges Of The 21st Century, Contributing Significantly To Chronic Diseases Such As Diabetes, Cardiovascular Disorders, And Metabolic Syndromes. Traditional Statistical Approaches And Black-box Machine Learning Models Have Been Widely Applied For Obesity Risk Prediction; However, Their Lack Of Interpretability Limits Trust, Clinical Acceptance, And Ethical Deployment In Healthcare Settings. This Research Proposes An Explainable Artificial Intelligence (XAI)-enhanced Machine Learning Framework For Obesity Risk Classification That Not Only Delivers Accurate Predictions But Also Provides Transparent And Human-interpretable Explanations For Decision Making. The System Integrates Advanced Machine Learning Classifiers With XAI Techniques To Uncover How Physiological, Behavioral , And Lifestyle Features Influence Obesity Risk. By Incorporating Explainability Methods Such As Feature Attribution And Local Explanation Models, The Proposed Approach Bridges The Gap Between Predictive Performance And Clinical Interpretability. The Framework Aims To Support Healthcare Professionals By Offering Clear Insights Into Contributing Risk Factors, Enabling Personalized Intervention Strategies And Early Prevention. Experimental Evaluation Demonstrates That The XAI-enhanced Models Achieve Competitive Accuracy While Significantly Improving Model Transparency And Trustworthiness. This Study Highlights The Importance Of Explainability In Healthcare-oriented Machine Learning Systems And Provides A Robust Foundation For Ethical, Reliable, And User-centric Obesity Risk Assessment Solutions.

Published:

01-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

43-47

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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