Developing a Machine Learning-Powered Dietary Guidance System for Diabetes Patients

Diabetes is a prevalent chronic condition that requires careful management of diet to control blood sugar levels and prevent complications. Traditional dietary planning can be complex and time-consuming, emphasizing the need for an efficient, personalized approach. This study presents the development of a machine learning-based dietary recommendation system tailored to individual patients with diabetes. The system uses patient data, including medical history, dietary preferences, and blood glucose levels, to provide personalized meal plans. Various machine learning models, including decision trees, random forests, and neural networks, are explored to identify the most effective algorithm for generating dietary recommendations. Evaluation metrics such as accuracy, precision, and recall are used to assess model performance. The results demonstrate that the system can successfully recommend meals that align with medical guidelines and improve diabetes management. This research paves the way for more effective, personalized, and scalable dietary planning for patients with diabetes.