• Shruti Chavan
• Arunima Garg
• Riya Mulay
• Prof. Rajani Jadhav

Diabetes is a chronic disease that affects millions of people worldwide. Diet plays a critical role in managing diabetes, but it can be difficult for people with diabetes to know what to eat and how much to eat. Diabetes meal recommendation systems can help people with diabetes make informed decisions about their meals. These systems use machine learning algorithms to recommend meals that are tailored to the individual's needs and preferences, taking into account factors such as their blood glucose levels, insulin doses, and dietary restrictions. This survey paper reviews the state of the art in diabetes meal recommendation systems. We discuss the different types of systems that have been developed, the machine learning algorithms that are used, and the performance of these systems in real-world settings. We also identify some of the challenges that need to be addressed in order to develop more effective and user-friendly diabetes meal recommendation systems.

Data-driven technology, Machine learning, Blood sugar monitoring, Meal recommendations, Glycemic control, Chronic disease management

"Exploring the landscape of diabetes dietary recommender systems: A Survey and Evaluation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.g60-g63, January-2024, Available :http://www.jetir.org/papers/JETIR2401608.pdf

"Exploring the landscape of diabetes dietary recommender systems: A Survey and Evaluation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppg60-g63, January-2024, Available at : http://www.jetir.org/papers/JETIR2401608.pdf