Cargando…
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...
Autores principales: | Arceo-Vilas, Alba, Fernandez-Lozano, Carlos, Pita, Salvador, Pértega-Díaz, Sonia, Pazos, Alejandro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924593/ https://www.ncbi.nlm.nih.gov/pubmed/33816938 http://dx.doi.org/10.7717/peerj-cs.287 |
Ejemplares similares
-
Machine learning analysis of TCGA cancer data
por: Liñares-Blanco, Jose, et al.
Publicado: (2021) -
A methodology for the design of experiments in computational intelligence with multiple regression models
por: Fernandez-Lozano, Carlos, et al.
Publicado: (2016) -
Cross-Cultural Adaptation of Mediterranean Diet Adherence Screener (MEDAS) Into Moroccan Arabic to Measure the Degree of Mediterranean Diet Adherence
por: Sammoud, Karima, et al.
Publicado: (2023) -
Drought stress detection technique for wheat crop using machine learning
por: Gupta, Ankita, et al.
Publicado: (2023) -
Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets
por: Dorn, Marcio, et al.
Publicado: (2021)