Hyperparameter Optimization: Achieving State-of-the-Art Caloric Burn Prediction with Fine-Tuned MLPRegressor

Accurately assessing caloric expenditure is essential for future advancement in personalized health tracking and exercise optimization. In this study, we develop a robust and scalable machine-learning framework that utilizes physiological and exercise-related metric data to achieve state-of-the art accuracy in the prediction of calorie burn. Through our proposed methodology, it forms a holistic integration of advanced data preprocessing, feature engineering, and hyperparameter optimization, serving as a multi-faceted approach to improve the predictive performance. Here we prefer a Multilayer Perceptron Regressor (MLPRegressor) with hyperparameter tuning to learn more complex, non-linear relationships that outperforms existing models like XGBoost and LightGBM across important metrics. In detail, MLPRegressor has an exceptional Math <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> equal to 1.0000, also the RMSE and MAE has the lowest number, 0.32uh and 0.27. Generalization of prediction is ensured by rigorous cross-validation, while explainability is provided with SHAP values to analyze feature impact and model decisions. We go beyond conventional regression-based methods, enabling integration with real-time wearable and digital health tools. Setting a new standard for caloric expenditure prediction, this study connects breakthrough advancements in machine learning with critical deployment in health informatics toward sustainable lifestyle modifications using precision analytics.