Classification and Prediction of Hypoglycemia in Patients with Type 2 Diabetes Mellitus Using Data from the EHR and Patient Context

Luis Claudio Gubert, Luis Claudio Gubert, Felipe Zeiser, Cristiano André da Costa, Rafael Kunst

2024

Abstract

The increase in obesity, a sedentary lifestyle, and population aging are considered the main factors for the increase in Type 2 Diabetes Mellitus (T2DM) worldwide. Global estimates indicate that around 400 million people live with T2DM, reaching 600 million in 2035. This scenario generates a high social and financial cost for the patient and the healthcare system. In this context, this work evaluates machine learning models to classify and predict hypoglycemic crises in patients with T2DM. A dataset with data from a clinical center in southern Brazil is constructed. Patient data involves Electronic Health Records (EHR) and data collected in the patient context through Internet of Things (IoT). This dataset is used to run classification and prediction models. Results show that the proposed approach is promising, achieving an AUC of 0.8200 and a sensitivity of 90.00% for classifying hypoglycemia. In addition, the Clarke Error Grid plot demonstrates an assertiveness of prediction for high blood glucose in clinical terms. These results demonstrate that the proposed method achieves comparable or superior results to related works in the literature. The combined use of EHR, IoT, and Machine Learning can be a promising alternative to improve the monitoring of chronic and long-term diseases, such as T2DM, contributing to a more accurate and effective diagnosis.

Paper Citation

in Harvard Style

Claudio Gubert L., Zeiser F., André da Costa C. and Kunst R. (2024). Classification and Prediction of Hypoglycemia in Patients with Type 2 Diabetes Mellitus Using Data from the EHR and Patient Context. In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-699-6, SciTePress, pages 276-283. DOI: 10.5220/0012705100003705

in Bibtex Style

@conference{iotbds24,
author={Luis Claudio Gubert and Felipe Zeiser and Cristiano André da Costa and Rafael Kunst},
title={Classification and Prediction of Hypoglycemia in Patients with Type 2 Diabetes Mellitus Using Data from the EHR and Patient Context},
booktitle={Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2024},
pages={276-283},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012705100003705},
isbn={978-989-758-699-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Classification and Prediction of Hypoglycemia in Patients with Type 2 Diabetes Mellitus Using Data from the EHR and Patient Context
SN - 978-989-758-699-6
AU - Claudio Gubert L.
AU - Zeiser F.
AU - André da Costa C.
AU - Kunst R.
PY - 2024
SP - 276
EP - 283
DO - 10.5220/0012705100003705
PB - SciTePress