The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis

Najib Ur Rehman, Ivan Contreras (), Aleix Beneyto and Josep Vehi ()
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Najib Ur Rehman: Modeling & Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, Universitat de Girona, 17003 Girona, Spain
Ivan Contreras: Modeling & Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, Universitat de Girona, 17003 Girona, Spain
Aleix Beneyto: Modeling & Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, Universitat de Girona, 17003 Girona, Spain
Josep Vehi: Modeling & Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, Universitat de Girona, 17003 Girona, Spain

Mathematics, 2024, vol. 12, issue 10, 1-23

Abstract: This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and real patient data. The study uses six commonly used machine learning models under varying conditions of missing samples, including custom and random patterns reflective of device failures and arbitrary data loss, with different levels of data removal before mealtimes. Additionally, the study explored different interpolation techniques to counter the effects of missing data samples. The research shows that missing samples generally reduce the model performance, but random forest is more robust to missing samples. The study concludes that the adverse effects of missing samples can be mitigated by leveraging complementary and informative non-point features. Consequently, our research highlights the importance of strategically handling missing data, selecting appropriate machine learning models, and considering feature types to enhance the performance of postprandial hypoglycemia predictions, thereby improving diabetes management.

Keywords: classification; data quality; hypoglycemia prediction; machine learning; postprandial hypoglycemia; type 1 diabetes; missing data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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