# Systematic Review of Nutritional Recommendation Systems
## 3 Results  
In the analyzed cases, we can see that most of the recommendation systems are designed to recommend different diets; some of them do not recommend whole diets but some specific foods. Rarely do we see systems that offer physical activity recommendations in addition to diet.  
On the other hand, most of the analyzed systems have made use of collaborative filtering. However, content-based, hybrid, and knowledge-based filtering have also been used. As for the type of platforms used, in most studies this was not specified, and in those where it was, online platforms were used.  
Finally, it should be noted that most studies and platforms have been motivated to help patients with a disease that may improve as a result of maintaining a healthier diet.  
Once the comparative table of the studies (Table 7) had been made, articles 1 to 11 were graded taking into account the criteria defined in Table 6 so that an overview of the quality of each article could be obtained. For this purpose, three levels of quality have been defined: High (H) the articles with a score between 8 and 11, Medium (M) between 5.1 and 7.9, and Low (L) less than 5. After the qualification, we can affirm that within the metrics used in this study, the articles [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] are of high impact.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **County** & **Type of Nutrition** & **Type of User** & **Recommendation** & **Developmental** & **Type of Platform** \\ \hline Design and Development of & & & & & & & \\ Diabetes Management & 2020 & Ghana & Nutrition for diabetes & Diabetes & & & \\ System Using Machine & & & & & & \\ Learning [16] & & & & & & \\ \hline Hybrid clustering-based & & & & & & \\ health decision-making for & 2019 & Korea & Normal & Everybody & Diet & Collaborative & Online/Offline \\ improving dietary & & & & & & & \\ habits [17] & & & & & & \\ \hline Lifestyle Classification for & & & & & & & \\ Recommendation of & & & & & & \\ Excessive Sugar & 2021 & Thailand & Excessive sugar & & & & \\ Consumption in Thai & & & & & & \\ Teangers [18] & & & & & & \\ \hline E-Health Monitoring System & & & & & Patients of & & \\ with Diet and Fitness & 2021 & India & USDA Food & Diabetes, Blood & Diet and Exercise & Collaborative & Online \\ Recommendation using & & & & & & & \\ Machine Learning [19] & & & & & & \\ \hline Enhanced predictive & & & & & & \\ learning approaches for & & & & & & \\ tweaked diet proposal & 2020 & India & Depending on the & & & & \\ system in health care [20] & & & & & & \\ \hline Realizing an Efficient & & & & & & \\ IoT-Assisted Patient Diet & & & & & & \\ Recommendation System & 2020 & England & Normal (Healthy) & Everybody & Diet & Knowledge- & N/O \\ Through Machine Learning & & & & & & \\ Model [21] & & & & & & \\ \hline A modular cluster-based & & & & & & \\ collaborative recommender & 2018 & Pakistan & Normal (Healthy) & Cardiac patients & Healthy habits & Collaborative & N/O \\ system for cardiac & & & & & & \\ patients [22] & & & & & & \\ \hline Intelligent Nutrition in & & & & & & \\ Healthcare and Continuous & 2019 & Portugal & Normal (Healthy) & Everybody & Diet & Knowledge- & N/O \\ Care [23] & & & & & & \\ \hline A DASH Diet & & & & & & \\ Recommendation System for & 2019 & Mauritius & Normal (Healthy) & Hypertensive & Diet & Content-based & Online \\ Hypertense Patients Using & & & & & & \\ Machine Learning [24] & & & & & & \\ \hline Food and Therapy & & & & & & \\ Recommendation System for & & & & & & \\ Autistic Syndrome using & 2019 & India & Normal (Healthy) & Children suffering from & Food and & & \\ Machine Learning & & & & & & \\ Techniques [25] & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Selected articles.