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This paper develops a machine learning model using gradient boosting to predict postprandial blood glucose levels in patients with gestational diabetes. The model uses data from meal diaries, food context, patient characteristics and questionnaires. Different models are trained and evaluated to predict the incremental area under the blood glucose curve two hours after eating. The best models have R values around 0.64 and mean absolute errors around 0.37 mmol/L*h. The most important predictors are meal glyce

Author

artemtvi

Language

EN