| Titre : |
Prediction of hypoglycemia episodes in type 1 diabetes patients |
| Type de document : |
document électronique |
| Auteurs : |
Mohamed Merouane Lakehal, Auteur ; Samia Beldjoudi, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2025 |
| Importance : |
1 fichier PDF (7 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel.Date Science et intelligence artificiel : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 96 - 101 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Hypoglycemia prediction
Continuous glucose monitoring (CGM)
Time series forecasting
Data imbalance
Diabetes management
Early warning system
Machine learning |
| Index. décimale : |
PI00725 |
| Résumé : |
Hypoglycemia, defined as a blood glucose level below 70 mg/dL, is a serious risk for individuals with Type 1 Diabetes Mellitus (T1DM), potentially causing severe outcomes such as seizures or unconsciousness if not addressed in time. Continuous Glucose Monitoring (CGM) systems, though less invasive than finger-prick testing, suffer from a physiological lag of 5–20 minutes between blood and interstitial glucose levels, limiting their ability to warn patients early.
This study proposes a predictive model to anticipate hypoglycemic events ahead of time, helping patients take preventive actions like carbohydrate intake. Using time-series CGM
data, the model explores both univariate (CGM-only) and multivariate (including insulin and carbohydrate intake) inputs. It also addresses the challenge of data imbalance, with
a focus on achieving high precision and recall to reduce false alarms.
Results show that univariate models perform comparably to multivariate ones, making them practical for real-world use. Regression-based models also generalize better across test conditions than classification models. The model’s clinical validity is supported by Clarke error grid analysis, where over 98% of predictions fall in safe zones (A and B). This approach supports safer, proactive diabetes management through timely, CGM-based hypoglycemia prediction. |
Prediction of hypoglycemia episodes in type 1 diabetes patients [document électronique] / Mohamed Merouane Lakehal, Auteur ; Samia Beldjoudi, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (7 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel.Date Science et intelligence artificiel : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 96 - 101 Langues : Anglais ( eng)
| Mots-clés : |
Hypoglycemia prediction
Continuous glucose monitoring (CGM)
Time series forecasting
Data imbalance
Diabetes management
Early warning system
Machine learning |
| Index. décimale : |
PI00725 |
| Résumé : |
Hypoglycemia, defined as a blood glucose level below 70 mg/dL, is a serious risk for individuals with Type 1 Diabetes Mellitus (T1DM), potentially causing severe outcomes such as seizures or unconsciousness if not addressed in time. Continuous Glucose Monitoring (CGM) systems, though less invasive than finger-prick testing, suffer from a physiological lag of 5–20 minutes between blood and interstitial glucose levels, limiting their ability to warn patients early.
This study proposes a predictive model to anticipate hypoglycemic events ahead of time, helping patients take preventive actions like carbohydrate intake. Using time-series CGM
data, the model explores both univariate (CGM-only) and multivariate (including insulin and carbohydrate intake) inputs. It also addresses the challenge of data imbalance, with
a focus on achieving high precision and recall to reduce false alarms.
Results show that univariate models perform comparably to multivariate ones, making them practical for real-world use. Regression-based models also generalize better across test conditions than classification models. The model’s clinical validity is supported by Clarke error grid analysis, where over 98% of predictions fall in safe zones (A and B). This approach supports safer, proactive diabetes management through timely, CGM-based hypoglycemia prediction. |
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