Blood Glucose Prediction Model for Type 1 Diabetes based on Extreme Gradient Boosting

Abstract

Predicting future blood glucose (BG) level for diabetic patients will help them to avoid critical conditions in the future. This study proposed Extreme Gradient Boosting (XGBoost), an ensemble learning model to predict the future blood glucose value of diabetic patients. The clinical dataset of Type 1 Diabetes (T1D) patients was utilized and the prediction models were generated to predict future BG of 30 and 60 minutes ahead of time. The prediction models have been tested tofive children who develop T1D and showed that BG prediction model based on XGBoost outperformed other models, with average of Root Mean Square Error (RMSE) are 23.219 mg/dL and 35.800 mg/dL for prediction horizon (PH) 30 and 60 minutes respectively. In addition, the result showed that by utilizing statistical-based features as additional attributes, most of the performance of predictions model were increased.

Machine Learning Approach for Postprandial Blood Glucose Prediction in Gestational Diabetes Mellitus

“…Feedforward neural networks, combinations of physiology-based models and machine learning techniques, recurrent neural networks and support vector machines appear to be the most frequently used algorithms for blood glucose prediction [5]. With the same data, in a direct comparison with other models, gradient boosting tends to show the most precise results [6]. Although different input parameters that might be beneficial for blood glucose prediction models were comprehensively discussed, specific data preprocessing, feature engineering and model tuning steps were not explained in detail in many of these papers.…”

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

“…The result showed that XGBoost outperformed logistic regression by achieving an accuracy of 75.7%. Alfian et al (2020) developed a model based on XGBoost to predict the future value of blood glucose for T1D patients [15]. Based on 30-and 60-min prediction horizon (PH) results, the XGBoost showed its superiority by achieving average of root mean square error (RMSE) of 23.219 mg/dL and 35.800 mg/dL for PH-30 and-60 min, respectively.…”

“…After selecting the best feature sets by utilizing the GA from the training data, the XGBoost is used to learn and generate the robust prediction model. Previous studies have reported the advantage of using XGBoost for predicting hepatitis B virus infection [13], gestational diabetes mellitus of early pregnant women [14], future blood glucose level of T1D patients [15], coronary artery calcium score (CACS) [16], and heart disease prediction [17]. XGBoost was proposed by Chen and Guestrin and is a scalable supervised machine learning algorithm based on the improvement of gradient boosting decision trees (GBDT) and used for regression and classification problems [12].…”

“…Extreme gradient boosting (XGBoost) is supervised machine learning algorithm based on the improvement of gradient boosting trees that can prevent overfitting [12]. Previous researchers have reported the advantage of XGBoost as classification model for predicting hepatitis B virus infection [13], gestational diabetes mellitus of early pregnant women [14], future blood glucose level of type 1 diabetes (T1D) patients [15], coronary artery calcium score (CACS) [16], and heart disease prediction [17]. However, in the machine learning research, the performance of the classification model may be influenced by unrelated attributes or features [18,19].…”

Development of SW Interface between Healthcare Standards—DASTA and HL7

“…These are just a few of the simplest examples of the use of new technologies in areas called smart health or mHealth. But much more sophisticated solutions are also emerging, such as big data analysis systems and appropriate treatments based on big data analysis and artificial intelligence technology; see Rhee et al, who proposed, for example, a blood glucose prediction model [25]. Additionally, an effective heart disease prediction model for a clinical decision support system [26] and the development of a disease prediction model based on the ensemble learning approach for diabetes and hypertension [27] have also been proposed.…”