Date of Publication :28th June 2024

Abstract:This study investigates how machine learning can integrate nutritional data into blood glucose prediction models for type 1 diabetes. Combining information about diet, physical activity, and past glucose levels, the project aims to improve prediction accuracy and provide a more complete picture of blood sugar dynamics. By analyzing a large dataset with advanced algorithms, the study reveals hidden connections between eating habits and blood sugar fluctuations. This allows for personalized predictions, empowering individuals to manage their health proactively. Through rigorous testing and comparisons, the research moves predictive modeling in diabetes care forward, offering promising ways to improve overall health. This holistic approach recognizes the complex nature of managing diabetes and emphasizes the importance of considering various factors that affect blood sugar. Ultimately, by providing personalized insights and actionable recommendations, this research helps to improve the quality of life for people with type 1 diabetes.

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