RG Hyperparameter Optimization Approach for Improved Indirect Prediction of Blood Glucose Levels by Boosting Ensemble Learning

This paper proposes an RG hyperparameter optimization approach, based on a sequential use of random search (R) and grid search (G), for improving the blood glucose level prediction of boosting ensemble learning models. An indirect prediction of blood glucose levels in patients is performed, based on historical medical data collected by means of physical examination methods, using 40 human body’s health indicators. The conducted experiments with real clinical data proved that the proposed RG double optimization approach helps improve the prediction performance of four state-of-the-art boosting ensemble learning models enriched by it, achieving 1.47% to 24.40% MSE improvement and 0.75% to 11.54% RMSE improvement.