Ontology-Based Nutritional Recommender System
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 rai...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/1/143 |
| _version_ | 1797499614580965376 |
|---|---|
| author | Dexon Mckensy-Sambola Miguel Ángel Rodríguez-García Francisco García-Sánchez Rafael Valencia-García |
| author_facet | Dexon Mckensy-Sambola Miguel Ángel Rodríguez-García Francisco García-Sánchez Rafael Valencia-García |
| author_sort | Dexon Mckensy-Sambola |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T03:49:56Z |
| format | Article |
| id | doaj.art-119e14924f44476c9cff6870c3846c5e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:49:56Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-119e14924f44476c9cff6870c3846c5e2023-11-23T11:08:22ZengMDPI AGApplied Sciences2076-34172021-12-0112114310.3390/app12010143Ontology-Based Nutritional Recommender SystemDexon Mckensy-Sambola0Miguel Ángel Rodríguez-García1Francisco García-Sánchez2Rafael Valencia-García3Escuela de Informática, Bluefields Indian & Caribbean University (BICU), Bluefields 81000, NicaraguaDepartamento de Ciencias de la Computación, Universidad Rey Juan Carlos, 28933 Madrid, SpainDepartamento de Informática y Sistemas, Universidad de Murcia, 30100 Murcia, SpainDepartamento de Informática y Sistemas, Universidad de Murcia, 30100 Murcia, SpainObesity 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%.https://www.mdpi.com/2076-3417/12/1/143diet recommendationbody mass indexoptimal nutritionontologyknowledge-based systems |
| spellingShingle | Dexon Mckensy-Sambola Miguel Ángel Rodríguez-García Francisco García-Sánchez Rafael Valencia-García Ontology-Based Nutritional Recommender System Applied Sciences diet recommendation body mass index optimal nutrition ontology knowledge-based systems |
| title | Ontology-Based Nutritional Recommender System |
| title_full | Ontology-Based Nutritional Recommender System |
| title_fullStr | Ontology-Based Nutritional Recommender System |
| title_full_unstemmed | Ontology-Based Nutritional Recommender System |
| title_short | Ontology-Based Nutritional Recommender System |
| title_sort | ontology based nutritional recommender system |
| topic | diet recommendation body mass index optimal nutrition ontology knowledge-based systems |
| url | https://www.mdpi.com/2076-3417/12/1/143 |
| work_keys_str_mv | AT dexonmckensysambola ontologybasednutritionalrecommendersystem AT miguelangelrodriguezgarcia ontologybasednutritionalrecommendersystem AT franciscogarciasanchez ontologybasednutritionalrecommendersystem AT rafaelvalenciagarcia ontologybasednutritionalrecommendersystem |