OptiX-CatBoost: An Optimized Explainable CatBoost Model for Analyzing the Effects of Health-Related Factors on Obesity

Obesity is a significant health concern linked to severe medical conditions. Obesity increases the risk of diabetes, thyroid issues, cardiac disease, liver tumors, and stroke. Early prediction of obesity risk is essential for improving public health and well-being. To address this, an optimized explain-able CatBoost model, referred to as OptiX-CatBoost, has been developed. Initially, various machine learning (ML) models are trained using the publicly available dhaka obesity dataset. Among these, the categorical boosting (CatBoost) model emerged as the top performer and is selected for optimization. Bayesian optimization is employed to fine-tune key hyperparameters of the CatBoost model, enhancing its performance. The experimental results demonstrated that the OptiX-CatBoost model outperformed existing methods, achieving an accuracy of 95.60%, a precision of 94.60%, a recall of 93.12%, an F1-score of 98.15%, a Jaccard score of 96.14%, and a Kappa score of 87.14%. Additionally, the predictions made by the proposed model are further analyzed using explainable artificial intelligence (XAI) techniques. Healthcare professionals can evaluate the predictions of the proposed model and learn more about the different factors driving obesity by using XAI techniques such as partial dependence graphs, permutation significance, and LIME (Local Interpretable Model-agnostic Explanations). This dual capability of accurate prediction and interpretability renders OptiX-CatBoost a valuable tool for early obesity risk assessment, ultimately aiding in the development of better public health strategies and interventions.