Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes
Full article
| Journal |
Diagnostics
ISSN: 2075-4418
|
| Output data |
Year: 2024,
Volume: 14,
Number: 21,
Article number
: 2427,
Pages count
: 11
DOI:
10.3390/diagnostics14212427
|
| Tags |
continuous glucose monitoring; glucose prediction; nocturnal hypoglycemia; cluster analysis; machine learning; random forest; gradient boosting trees |
| Authors |
Kladov Danil E.
1,2
,
Berikov Vladimir B.
1,2
,
Semenova Julia F.
2
,
Klimontov Vadim V.
2
|
| Affiliations |
| 1 |
Laboratory of Data Analysis, Sobolev Institute of Mathematics, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
|
| 2 |
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
|
|
Funding (1)
|
1
|
Russian Science Foundation
|
20-15-00057
|
Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction