Utilizing deep neural network for web-based blood glucose level prediction system

Abstract

Machine learning algorithms can be used to forecast future blood glucose (BG) levels for diabetes patients, according to recent studies. In this study, dataset from continuous glucose monitoring (CGM) system was used as the sole input for the machine learning models. To forecast blood glucose levels 15, 30, and 45 minutes in the future, we suggested deep neural network (DNN) and tested it on 7 patients with type 1 diabetes (T1D). The suggested prediction model was evaluated against a variety of machine learning models, such as k-nearest neighbor (KNN), support vector regression (SVR), decision tree (DT), adaptive boosting (AdaBoost), random forest (RF), and eXtreme gradient boosting (XGBoost). The experimental findings demonstrated that the proposed DNN model outperformed all other models, with average root mean square errors (RMSEs) of 17.295, 25.940, and 35.146 mg/dL over prediction horizons (PHs) of 15, 30, and 45 minutes, respectively. Additionally, we have included the suggested prediction model in web-based blood glucose level prediction tools. By using this web-based system, patients may readily acquire their future blood glucose levels, allowing for the generation of preventative alarms prior to crucial hypoglycemia or hyperglycemic situations.

Predictive maintenance framework for assessing health state of centrifugal pumps

“…Additionally, a similar framework was implemented in [41] for diabetes prediction with the presented approach resulting in an area under curve (AUC) of 82%. However, in the cases of [42] and [43] facing glycose levels predictions, XGBoost was not considered the optimal solution and DNN and RF were the selected algorithms respectively. Furthermore, the XGBoost algorithm was selected for pregnancy risk monitoring with 96% accuracy [44] whereas an improvement of the proposed approach combining CNN and XGBoost methodology was proposed for renal stone diagnosis [45], breast cancer detection [46] and image classification [47] with accuracies of 99.5 %.…”

Optimized stacking ensemble for early-stage diabetes mellitus prediction

“…Composed of interconnected nodes called neurons, organized in layered architectures, ANNs aim to unravel intricate relationships between input features and target variables. ANN can capture non-linear relationships, but require careful architecture design, and training data, and can be computationally intensive [33], [34]. In WEKA, researchers can utilize the MultilayerPerceptronClassifier to implement ANNs.…”