Glucosense Ecosystem: An Intelligent Clinical Insulin Decision Support and Nutrition Platform for Diabetes Care.

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dc.contributor.authorJoy Abaho
dc.contributor.authorGodfrey Mucunguzi
dc.contributor.authorBachawa Wangolo
dc.date.accessioned2026-09-02T09:19:20Z
dc.date.available2026-09-02T09:19:20Z
dc.date.issued2026-06-04
dc.descriptionUndergraduate
dc.description.abstractType 1 diabetes is one of the most surging health condition with an approximate number of 369,100 adult cases in Uganda by 2024. There is still a lack of adaptive and personalized tools necessary to manage the disease. The GlucoSense ecosystem focuses on an integrated, full-stack with machine learning to predict insulin intakes, a nutritional system that offers meal recommendations, a meal chatbot, and glucose-guided foods for diabetic patients. The project was built on a three-tier architecture with the frontend layer developed in ReactVite for both the Clinical portal and the Meal plan User Interface, the backend layer follows a FastAPI design and the database layer was built in SQLite and SQLAlchemy. The results show that the Linear Regression model achieved the lowest error values in predicting insulin dosage with a Mean Absolute Error of 2.98, Mean Squared Error of 11.524 and a Root Mean Squared Error of 3.3947. These results proved that linear Regression was the most stable model and produced fewer dangerous miscalculations compared to random forest, gradient boosting and xgboost. However, all the models showed very low negative R2 values which would probably have occurred because of feature limitations since most features might not have been fully captured.
dc.identifier.urihttps://hdl.handle.net/20.500.12311/3609
dc.language.isoen
dc.publisherUganda Christian University
dc.subjectHealth Informatics
dc.subjectPredictive Analytics
dc.subjectMachine Learning
dc.subjectInsulin Dosage Prediction
dc.subjectPersonalized Healthcare
dc.subjectNutritional Recommendation System
dc.subjectMeal Planning
dc.subjectDigital Health
dc.subjectGlucose Monitoring
dc.subjectClinical Decision Support
dc.subjectInsulin Dose Classification
dc.subjectExplainable AI
dc.subjectSHAP
dc.subjectXGBoost
dc.subjectDiabetes Management
dc.subjectRetrieval Augmented Generation
dc.subjectFull-Stack Healthcare System
dc.subjectUganda
dc.titleGlucosense Ecosystem: An Intelligent Clinical Insulin Decision Support and Nutrition Platform for Diabetes Care.
dc.typeDissertation

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Bachelor of Science in Computer Science