Exploring demographic importance for hypoglycemia classification leveraging DiaData
Publication date
2025-12-11
Document type
Conference paper
Author
Organisational unit
Conference
25th International Conference on Bioinformatics and Bioengineering (BIBE) 2025 ; Athens, Greece ; November 6-8, 2025
Publisher
IEEE
Book title
Conference proceedings: 2025 IEEE 25th International Conference on Bioinformatics and Bioengineering (BIBE)
First page
248
Last page
255
Part of the university bibliography
✅
Language
English
Keyword
DiaData
Hypoglycemia classification
Personal features
Type 1 Diabetes
Abstract
Personal features can significantly enhance machine learning models by improving both personalization and prediction performance in health-related applications. In this study, we created a new subset of DiaData, an integrated continuous glucose monitoring (CGM) dataset of 2510 subjects with Type 1 Diabetes (T1D), by extracting age, sex, duration of diabetes, HbA1c, race, height, and weight. Our objective was to assess the influence of these additional features on the performance of hypoglycemia classification models. T1D is an autoimmune disorder in which the pancreas cannot produce sufficient insulin, and thereby affected individuals depend on external insulin injections. However, a major side effect of insulin therapy is hypoglycemia, defined as blood glucose levels below 70 mg/dL. Hypoglycemia can become life-threatening if not detected on time and if appropriate preventive measures are not implemented. This risk is particularly concerning in asymptomatic episodes. To address this challenge, we propose Fully Convolutional Network (FCN) and XGBoost-based hypoglycemia classification models that incorporate personal features. Specifically, our contributions include: 1) Enriching DiaData with additional personal features by extracting a new subset comprising 8 databases and 1651 subjects. 2) Performing a correlation analysis between numerical personal features and hypoglycemic CGM glucose ranges. 3) Training both deep learning and machine learning models to explore the impact of personal features on prediction performance. 4) Evaluating the contribution of personal features to prediction outcomes in XGBoost models. Our findings show that while the HbA1c score and the age group have a moderate impact, other features such as height, weight, and the duration of diabetes have minimal influence. Although personal features were included, their addition did not lead to a significant improvement in the models' predictive performance.
Version
Published version
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