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

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] Analizar o estado nutricional e a adhesión á dieta mediterránea pode servir para determinar estados de vulnerabilidade nutricional e identificar patoloxías asociadas como poden ser trastornos alimenticios ou sicolóxicos. Polo que esta Tese de doutoramento céntrase por una banda, na recollida de información mediante entrevista persoal, exploración física, revisión das historias clínicas de atención primaria, así como na cumplimentación de diferentes cuestionarios: Mini Nutritional Assessment (MNA), índice de comorbilidade de Charlson, test de adhesión á dieta mediterránea, percepción subxectiva de peso, e as Subescalas EDI2 (insatisfacción corporal e obsesión pola delgadeza). Por outra banda, analizarase todas estas variables heteroxéneas seguindo un deseño experimental clínico baseado en test estatísticos e se comparan os resultados con diferentes aproximacións de Intelixencia Artificial (en adiante IA), como o Machine Learning (en adiante ML) podendo despois discutir as diferencias entre ambas. A través desta comparación compróbase se se chegan ás mesmas conclusións a través das diferentes técnicas.[Resumen] Analizar el estado nutricional y la adherencia a la dieta mediterránea puede servir para determinar estados de vulnerabilidad nutricional e identificar patologías asociadas como pueden ser trastornos alimenticios o psicológicos. Por lo que esta Tesis Doctoral se centra, por un lado, en la recogida de información mediante entrevista personal, exploración física, revisión de las historias clínicas de atención primaria, así como en la cumplimentación de diferentes cuestionarios: Mini Nutritional Assessment (MNA), índice de comorbilidad de Charlson, test de adherencia a la dieta mediterránea, percepción subjetiva de peso, y las Subescalas del EDI2 (insatisfacción corporal y obsesión por la delgadez). Por otro lado, se analizan todas estas variables heterogéneas siguiendo un diseño experimental clínico basado en test estadísticos y se comparan los resultados con diferentes aproximaciones de Inteligencia Artificial (en adelante IA), como el Machine Learning (en adelante, ML) pudiendo después discutir las diferencias entre ambas. A través de ésta comparativa se comprueba si se llega a las mismas conclusiones a través de las diferentes técnicas.[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

Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques

[Abstract] Food consumption patterns have undergone changes that in recent years have resulted in serious health problems. Studies based on the evaluation of the nutritional status have determined that the adoption of a food pattern-based primarily on a Mediterranean diet (MD) has a preventive role, as well as the ability to mitigate the negative effects of certain pathologies. A group of more than 500 adults aged over 40 years from our cohort in Northwestern Spain was surveyed. Under our experimental design, 10 experiments were run with four different machine-learning algorithms and the predictive factors most relevant to the adherence of a MD were identified. A feature selection approach was explored and under a null hypothesis test, it was concluded that only 16 measures were of relevance, suggesting the strength of this observational study. Our findings indicate that the following factors have the highest predictive value in terms of the degree of adherence to the MD: basal metabolic rate, mini nutritional assessment questionnaire total score, weight, height, bone density, waist-hip ratio, smoking habits, age, EDI-OD, circumference of the arm, activity metabolism, subscapular skinfold, subscapular circumference in cm, circumference of the waist, circumference of the calf and brachial area.Instituto de Salud Carlos III; PI17/01826Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques

Food consumption patterns have undergone changes that in recent years have resulted in serious health problems. Studies based on the evaluation of the nutritional status have determined that the adoption of a food pattern-based primarily on a Mediterranean diet (MD) has a preventive role, as well as the ability to mitigate the negative effects of certain pathologies. A group of more than 500 adults aged over 40 years from our cohort in Northwestern Spain was surveyed. Under our experimental design, 10 experiments were run with four different machine-learning algorithms and the predictive factors most relevant to the adherence of a MD were identified. A feature selection approach was explored and under a null hypothesis test, it was concluded that only 16 measures were of relevance, suggesting the strength of this observational study. Our findings indicate that the following factors have the highest predictive value in terms of the degree of adherence to the MD: basal metabolic rate, mini nutritional assessment questionnaire total score, weight, height, bone density, waist-hip ratio, smoking habits, age, EDI-OD, circumference of the arm, activity metabolism, subscapular skinfold, subscapular circumference in cm, circumference of the waist, circumference of the calf and brachial area