| Titre : | Prediction of hypoglycemia episodes in type 1 diabetes patients |
| Auteurs : | Mohamed Merouane Lakehal, Auteur ; Samia Beldjoudi, Directeur de thèse |
| Type de document : | document électronique |
| Editeur : | [S.l.] : [s.n.], 2025 |
| Format : | 1 fichier PDF (7 Mo) / ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel. Data Science-Intelligence Artificielle : Alger, École Nationale Polytechnique : 2025 Bibliogr. p. 96 - 101 |
| Langues : | Anglais |
| Index. décimale : | PI00725 |
| Tags : | Hypoglycemia prediction Continuous glucose monitoring (CGM) Time series forecasting Data imbalance Diabetes management Early warning system Machine learning |
| 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. |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité | Spécialité | Etat_Exemplaire |
|---|---|---|---|---|---|---|
| PI00725 | Ressources électroniques | Bibliothèque centrale | Projet Fin d'Etudes | Disponible | Data sciences_Intelligence artificielle | Téléchargeable |
