@article{uneatlantico5420,
            year = {2023},
         journal = {Journal of Medical Systems},
          volume = {47},
          author = {Antonio Ferreras and Sandra Sumalla Cano and Rosmeri Mart{\'i}nez-Licort and I{\~n}aki El{\'i}o Pascual and Kilian Tutusaus and Thomas Prola and Juan Luis Vidal Maz{\'o}n and Benjam{\'i}n Sahelices and Isabel de la Torre D{\'i}ez},
           title = {Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight},
          number = {1},
        abstract = {Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics.},
             url = {http://repositorio.uneatlantico.es/id/eprint/5420/},
        keywords = {Nutrition; Health; Machine learning; Obesity; Overweight}
}