Supervised Machine Learning Model for Diabetic Students’ Glucose Levels Classification System

Accurate and timely blood glucose prediction is essential for students who have been diagnosed with diabetes to prevent hypoglycemia and hyperglycemia episodes throughout the school days. Continuous Glucose Monitoring (CGM) is utilized in clinical situations to manage blood sugar levels effectively which is a form of sensor that can be incorporated in the Internet of Things (IoT). In addition, machine learning (ML) models can precisely be used for predicting glucose levels using a CGM system. We aim to develop a decision support system based on ML algorithms and the IoT, for which we have applied Random Forest, Logistic Regression, AdaBoost (Ada), Multi-layer perceptron (MLP), and linear SVM algorithms, followed by a majority voting algorithm. Evaluation metrics such as the Confusion Matrix, Accuracy, Precision, F1 score and Recall, were implemented to evaluate the performance of each algorithm. The experimental results demonstrate that the accuracy of the majority voting model after five-fold cross-validations is 99.61%, which ideally achieves higher performance than any single model.