Estado nutricional y adherencia a la dieta mediterránea en población mayor de 40 añosutilidad de las técnicas de inteligencia artificial versus técnicas estadísticas clásicas

Abstract

Analyzing the nutritional status and adherence to the Mediterranean diet can be used to determine states of nutritional vulnerability and identify associated pathologies such as eating or psychological disorders. So this Doctoral Thesis focuses, on the one hand, on the collection of information through personal interview, physical examination, review of the primary care clinical records, as well as on the completion of different questionnaires: Mini Nutritional Assessment (MNA), Charlson comorbidity index, test of adherence to the Mediterranean diet, subjective perception of weight, and EDI2 subscales (body dissatisfaction and obsession with thinness). On the other hand, all these heterogeneous variables are analyzed following a clinical experimental design based on statistical tests and the results are compared with different Artificial Intelligence (hereinafter, IA) approaches, such as Machine Learning (hereinafter, ML) and can then discuss the differences between them. Through this comparison, it is checked whether the same conclusions are reached through the different techniques.