RESUMEN
Introduction: Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs-the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion. Methods: Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost. Results: The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents. Discussion: Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.
RESUMEN
Introducción: La calidad en los servicios de salud no pasa solamente por el desarrollo tecnológico y el conocimiento especializado de nuestros trabajadores del sector. Existen factores que influyen notablemente en el alcance de la excelencia en estos centros. Uno de estos aspectos es la educación sanitaria y el conocimiento que tengan los colectivos laborales sobre los riesgos y las causas que pueden producir la transmisión de enfermedades y su relación con las malas prácticas que se pueden generar durante esta tarea. Material y método: Se encuesta el universo de trabajadores de 3 policlínicos del municipio Playa (Manuel Fajardo', 28 de Enero' y 5 de Septiembre'), a partir de un instrumento breve de fácil aplicación que explora la percepción que los trabajadores tienen sobre su actividad. El instrumento fue aplicado por un equipo de trabajo a 304 trabajadores, de ellos 240 mujeres y 65 hombres, que se encontraban en activo en el momento de la encuesta. Resultados: El universo de trabajadores resultó un grupo joven con alta experiencia en el sector y un bajo reconocimiento sobre los riesgos que representan potencialmente algunas condiciones de trabajo. Se asoció al nivel de capacitación declarado y el reconocimiento de los riesgos solo en el policlínico Manuel Fajardo. Un alto porcentaje de los trabajadores en los tres policlínicos declara no haber recibido nunca capacitación sobre el tema. Se calcularon dos ecuaciones discriminantes que nos hablan de diferencias entre los que recibieron y los que no recibieron capacitación, con buen poder discriminativo. Conclusiones: No hay un buen conocimiento y es necesaria la capacitación sobre los riesgos del trabajo en la población estudiada(AU)