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Article

Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes

by
Roman M. Kozinetz
,
Vladimir B. Berikov
,
Julia F. Semenova
and
Vadim V. Klimontov
*
Laboratory of Endocrinology, Research Institute of Clinical and Experimental Lymphology—Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (RICEL–Branch of IC&G SB RAS), 630060 Novosibirsk, Russia
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(7), 740; https://doi.org/10.3390/diagnostics14070740
Submission received: 27 December 2023 / Revised: 6 March 2024 / Accepted: 28 March 2024 / Published: 30 March 2024
(This article belongs to the Special Issue Machine Learning Models in Diagnosis and Treatment of Diabetes)

Abstract

Glucose management at night is a major challenge for people with type 1 diabetes (T1D), especially for those managed with multiple daily injections (MDIs). In this study, we developed machine learning (ML) and deep learning (DL) models to predict nocturnal glucose within the target range (3.9–10 mmol/L), above the target range, and below the target range in subjects with T1D managed with MDIs. The models were trained and tested on continuous glucose monitoring data obtained from 380 subjects with T1D. Two DL algorithms—multi-layer perceptron (MLP) and a convolutional neural network (CNN)—as well as two classic ML algorithms, random forest (RF) and gradient boosting trees (GBTs), were applied. The resulting models based on the DL and ML algorithms demonstrated high and similar accuracy in predicting target glucose (F1 metric: 96–98%) and above-target glucose (F1: 93–97%) within a 30 min prediction horizon. Model performance was poorer when predicting low glucose (F1: 80–86%). MLP provided the highest accuracy in low-glucose prediction. The results indicate that both DL (MLP, CNN) and ML (RF, GBTs) algorithms operating CGM data can be used for the simultaneous prediction of nocturnal glucose values within the target, above-target, and below-target ranges in people with T1D managed with MDIs.
Keywords: type 1 diabetes; continuous glucose monitoring; glucose range; prediction; machine learning; deep learning; neural networks; random forest; boosting trees type 1 diabetes; continuous glucose monitoring; glucose range; prediction; machine learning; deep learning; neural networks; random forest; boosting trees

Share and Cite

MDPI and ACS Style

Kozinetz, R.M.; Berikov, V.B.; Semenova, J.F.; Klimontov, V.V. Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes. Diagnostics 2024, 14, 740. https://doi.org/10.3390/diagnostics14070740

AMA Style

Kozinetz RM, Berikov VB, Semenova JF, Klimontov VV. Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes. Diagnostics. 2024; 14(7):740. https://doi.org/10.3390/diagnostics14070740

Chicago/Turabian Style

Kozinetz, Roman M., Vladimir B. Berikov, Julia F. Semenova, and Vadim V. Klimontov. 2024. "Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes" Diagnostics 14, no. 7: 740. https://doi.org/10.3390/diagnostics14070740

APA Style

Kozinetz, R. M., Berikov, V. B., Semenova, J. F., & Klimontov, V. V. (2024). Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes. Diagnostics, 14(7), 740. https://doi.org/10.3390/diagnostics14070740

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