# Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData

arXiv: https://arxiv.org/abs/2511.02849 · HTML: https://mlsanity.com/paper/2511.02849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions](https://mlsanity.com/paper/2304.06506/) (2023)
- [GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks](https://mlsanity.com/paper/2410.05780/) (2024)
- [Deep Learning-Based Hypoglycemia Classification Across Multiple Prediction Horizons](https://mlsanity.com/paper/2504.00009/) (2025)
- [Presenting DiaData for Research on Type 1 Diabetes](https://mlsanity.com/paper/2508.09160/) (2025)
- [Evaluating Imputation Techniques for Short-Term Gaps in Heart Rate Data](https://mlsanity.com/paper/2508.08268/) (2025)

## Built on by (most cited first, 2 of 2)

- [MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management](https://mlsanity.com/paper/2601.11505/) (2026)
- [Impact of Age Specialized Models for Hypoglycemia Classification](https://mlsanity.com/paper/2604.23732/) (2026)

## Abstract (arXiv)

Individualized therapy is driven forward by medical data analysis, which provides insight into the patient's context. In particular, for Type 1 Diabetes (T1D), which is an autoimmune disease, relationships between demographics, sensor data, and context can be analyzed. However, outliers, noisy data, and small data volumes cannot provide a reliable analysis. Hence, the research domain requires large volumes of high-quality data. Moreover, missing values can lead to information loss. To address this limitation, this study improves the data quality of DiaData, an integration of 15 separate datasets containing glucose values from 2510 subjects with T1D. Notably, we make the following contributions: 1) Outliers are identified with the interquartile range (IQR) approach and treated by replacing them with missing values. 2) Small gaps (≤ 25 min) are imputed with linear interpolation and larger gaps (≥ 30 and < 120 min) with Stineman interpolation. Based on a visual comparison, Stineman interpolation provides more realistic glucose estimates than linear interpolation for larger gaps. 3) After data cleaning, the correlation between glucose and heart rate is analyzed, yielding a moderate relation between 15 and 60 minutes before hypoglycemia (≤ 70 mg/dL). 4) Finally, a benchmark for hypoglycemia classification is provided with a state-of-the-art ResNet model. The model is trained with the Maindatabase and Subdatabase II of DiaData to classify hypoglycemia onset up to 2 hours in advance. Training with more data improves performance by 7% while using quality-refined data yields a 2-3% gain compared to raw data.