Evaluating Time Series Classification Models for Nocturnal Hypoglycemia: From Predictive Performance to Environmental Impact

Abstract

Type 1 Diabetes (T1D) is an autoimmune condition that results in an insulin deficiency. People with T1D require the administration of exogenous insulin to maintain target glucose levels. However, insulin therapy can cause hypoglycemic episodes, which occur when blood glucose levels fall below 70 mg/dL. Nocturnal Hypoglycemia (NH) occurs while the individual is asleep and can lead to different clinical complications. Developing predictive approaches to predict NH before sleep could reduce these episodes and mitigate acute complications. While numerous models exist for Time Series Classification (TSC), their use for NH prediction remains limited. This study evaluates 14 different TSC models for NH prediction, assessing their performance by evaluating classification metrics, computational time, and environmental impact (measured by energy consumption and $CO_{2}$ emissions). The approaches include distance-based, convolutional-based, deep learning, dictionary-based, feature-based, shapelet-based, and interval-based methods. We employed glucose data from 52 individuals with T1D. Experimental results showed that interval-based and feature-based approaches achieved the best predictive performance, obtaining the highest Area Under the Curve Operator (AUCROC) of 0.703. Additionally, both demonstrated low environmental impact due to their short computational time. However, substantial differences in environmental impact were observed depending on the approach. Distance-based methods and deep learning approaches exhibited the highest environmental impact. This paper provides key insights into the effectiveness of TSC models for NH prediction, highlighting the trade-off between model performance and environmental impact.