A Personalized Blood Glucose Prediction Model Using Random Forest Regression

Predicting future blood glucose (BG) values for diabetic patients, particularly for type 1 diabetes (T1D), remains an important and challenging issue. To overcome it, several well-known machine learning models have been used in recent years. Thus, a personalized model based by using random forest (RF) regression is implemented to forecast the future BG level of T1D patients. To create the models and forecast the BG value, a clinical dataset of T1D patients is employed. In this study, the future BG value, or prediction horizon (PH), is used for the next 15 and 30 minutes. Several performance metrics, including coefficient of determination (R
²
), mean absolute percentage error (MAPE), and root mean square error (RMSE), are measured during the experiments to calculate the prediction models' performances. The results revealed that the proposed BG prediction model outperformed other models including Multi-Layer Perceptron, Support Vector Regression, Decision Tree, K-Nearest Neighbour, with an average RMSE, MAPE, and R
²
of 15.54 mg/dL, 8.94%, 0.88, and 27.61 mg/dL, 15.52%, and 0.66 for PH of 15 and 30 minutes, respectively. In addition, the RMSE score was reduced by around 1.14 mg/dL and 1.32 mg/dL for the next 15 and 30 minutes, respectively, after adding statistical-extracted data as additional features for the regression models, compared to regression models without statistical data. Ultimately, it is expected that the results of the present study could be used to improve diabetes care.