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A Comparison of Machine Learning Algorithms in Blood Glucose Prediction for People with Type 1 Diabetes

Diabetes is a metabolic disease with the characteristic of hyperglycemia. The pathogenic principle is derived from the defect of insulin secretion or the impairment of biological effects, or both. We use machine learning models and deep learning models for forecasting future blood glucose levels in this paper, and study the efficiency of detecting hypoglycemia and hyperglycemia events. The data set used is in-silico data generated from the UVA/PADOVA type 1 diabetes simulator. We aim to compare support vector machines, random forests, linear regression, K-Nearest Neighbors regression (KNN), XGBoosted trees and other deep learning models in terms of Root Mean Squared Error (RMSE), and other several evaluation metrics to study their effectiveness in predicting future blood sugar, and the accuracy rate of predicting hypoglycemia and hyperglycemia events. In this work, we found a bidirectional long-short term memory (LSTM) model with the best prediction effect, which can predict the blood glucose level of simulated patients with leading accuracy within 30 minutes (RMSE = 7.55±0.19 [mg/dl], R2-SCORE=0.96). The hopeful results show that this method could have practical application value for self-management of blood glucose in patients with type 1 diabetes.