Understanding Machine Learning & its Application in Obesity Estimation by Explainable AI
Mahin Khan Mahadi, Rummanur Rahad, Abdullah Abdullah, Abu Noman, Samiha Ishrat, Fahim Faisal
Abstract
The escalating prevalence of obesity poses a formidable challenge to global public health, necessitating innovative approaches for accurate estimation and management. This research addresses this imperative by introducing an Explainable Artificial Intelligence (XAI) framework to estimate obesity levels. Meticulous hyperparameter tuning has resulted in elevated performance metrics, including accuracy, weighted precision, weighted recall, and weighted F1-score. Leveraging a decision support system, a robust machine learning model is developed exhibiting an impressive cross-validation accuracy of 97.39%. The model seamlessly integrates data on eating habits and physical condition, demonstrating improved performance in estimating obesity levels. The application of SHAP analysis unveils critical features within the dataset, thereby augmenting model interpretability and trustworthiness. The outcomes of this study provide a reliable tool for physicians, contributing to more informed clinical decisions in obesity management.