BLOOD GLUCOSE LEVEL PREDICTION USING RANDOM FOREST AND XGBOOST
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Abstract
Accurate blood glucose level prediction is crucial for effective diabetes management, enabling timely interventions and reducing complications. The limitations of traditional machine learning models like Random Forest and XGBOOST include the inability to deal with noisy data and datasets with imbalances. This study proposes a hybrid ensemble model that combines Random Forest for robust feature selection and initial predictions with XGBOOST to improve accuracy by focusing on instances that have been misclassified. The hybrid approach improves the handling of imbalanced and noisy datasets, achieving better accuracy and generalization. The model's superiority over standalone machine learning models is demonstrated by experimental results on publicly accessible datasets, highlighting its potential as a reliable method for predicting blood glucose levels and supporting diabetes management.
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