Ontology-Based Nutritional Recommender System - Mckensy-Sambola, Dexon; Rodríguez-García, Miguel Ángel; García-Sánchez, Francisco; Valencia-García, Rafael (Applied sciences, 2022)
Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engine capable of identifying the different levels of overweight and obesity in users and providing dietary strategies to mitigate them. To do so, we defined the Ontology of Dietary Recommendations (ODR) with axioms to model recipes, ingredients, and a set of diets to assist people who suffer from obesity. We validated the defined model by using a real set of individuals who were anonymized. A panel of advisors evaluated each individual record and suggested the most appropriate diets from those included in the ontology. Then, the proposed system was asked to also provide diet recommendations for each individual, which were compared with those proposed by the advisors (ground truth), reaching a mean accuracy of 87%.