A Performance Analysis of RNN Algorithms for Accurate Hypoglycemia Prediction in Type-1 Diabetes

Abstract

Listen

Translate article

Diabetes is a chronic disease originating from preeminent blood glucose levels where there is an impairment in the ability of the human body to produce or effectively use insulin, therefore resulting in possible complications to various organ systems. Hypoglycemia can be defined as a critical condition in diabetes that is characterized by very low levels of blood sugar and can emanate from any imbalance between insulin, glucose, and external factors such as medication or physical activity. The prediction of hypoglycemia is a very important task in diabetes management, which encompasses sophisticated technologies and deep-learning systems, such as RNNs, in performing analyses on patient-specific data. These must provide timely warnings to avoid hazardous blood sugar dips. This paper takes a close look at the performance of RNN in the case of accurate hypoglycemia prediction among patients with Type-1 Diabetes, considering a dataset from Shanghai T1DM. In this work, three different RNN architectures are considered for performance evaluation: Long-Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple RNN. The main goal was to compare their predictive performances in forecasting hypoglycemic events, which is an issue of utmost relevance when it comes to proactive diabetes management. The results show generally variable performances by the RNN models. Overall, GRU performed with striking accuracy in hypoglycemia predictions, while LSTM had high specificity. These findings underline that various metrics should be considered for the comprehensive evaluation of predictive models in the management of diabetes. It will give an idea about various RNN algorithms, strengths, and weaknesses to develop more effective and personalized strategies related to hypoglycemia prediction in Type-1 Diabetes.