Fuzzy Logic Based Personalised Diet Recommendation Engine for Dietary Prevention and Control of Diabetics

Abstract

Dietary management is a cornerstone in the prevention and treatment of Type 2 diabetes mellitus. Despite advancements in understanding dietary approaches, many patients rely on generalized advice rather than individualized plans. The affordability and accessibility of nutritious food remain significant barriers in low- and middle-income settings. Additionally, there is limited integration of technology-based tools into routine care to assist healthcare providers in delivering personalized dietary recommendations. The aim of this research is to develop a personalized food mapping system that aligns dietary recommendations with the health conditions and preferences of diabetes patients. The system derives its framework from the Nigerian food composition table thus providing culturally appropriate advice to its users. Analysing nutritional values together with local food glycemic indexes enables users to identify more suitable dietary choices which both match their nutritional requirements and food tastes. An individualised dietary system helps users maintain their planned meals more easily and these strategies work to preserve blood sugar levels. By providing recommendations that are truly individualized, it can help people better manage their blood sugar and overall health, which is essential for preventing serious long-term complications. Beyond diabetes, this personalized diet system could serve as a model for managing other health conditions. It offers a more effective way to give dietary advice, improving patient outcomes and making healthcare more efficient. Ultimately, this approach could make it easier for healthcare providers and patients alike to manage diet-related health challenges in a way that feels more personalized and adaptable.