Food Recommendation System Using Clustering Analysis for Diabetic Patients

Food and nutrition are a key to have good health. They are important for everyone to maintain a healthy diet especially for diabetic patients who have several limitations. Nutrition therapy is a major solution to prevent, manage and control diabetes by managing the nutrition based on the belief that food provides vital medicine and maintains a good health. Typically, diabetic patients need to avoid additional sugar and fat so the food pyramid is recommended to the patients for finding the substitution from the same food group. However, there is still a dietary diversity within food groups that can affect the diabetic patients. In this study, we proposed Food Recommendation System (FRS) by using food clustering analysis for diabetic patients. Our system will recommend the proper substituted foods in the context of nutrition and food characteristic. We used Self-Organizing Map (SOM) and K-mean clustering for food clustering analysis which is based on the similarity of eight significant nutrients for diabetic patient. At the end, the FRS was evaluated by nutritionists and it has performed very well and useful for nutrition area.