Explainable AI (XAI) for Obesity Prediction: An Optimized MLP Approach with SHAP Interpretability on Lifestyle and Behavioral Data

Obesity represents a major public health challenge, requiring accurate and interpretable predictive tools. This study proposes an approach based on a Multilayer Perceptron (MLP) optimized to predict obesity levels from lifestyle data, eating habits, and physiological characteristics, using a comprehensive Kaggle dataset combining real and synthetic samples. After rigorous preprocessing, including normalization and class rebalancing, we compare the performance of the MLP with four classical algorithms (Logistic Regression, KNN, Random Forest, and XGBoost) using comprehensive metrics (accuracy, precision, recall, F1-score, AUC-ROC). The results demonstrate the superiority of the optimized MLP (98.4% accuracy, F1-score of 0.97) over the other models, with a significant improvement from hyperparameter optimization through GridSearchCV. The XAI analysis via SHAP identifies weight, gender, height, and physical activity as the most determinant factors, providing crucial transparent explanations for clinical applications. This combination of high predictive performance and interpretability makes the MLP a valuable tool for obesity prevention and diagnosis in public health.