TY  - JOUR
Y1  - 2023///
IS  - 1
KW  - Nutrition; Health; Machine learning; Obesity; Overweight
AV  - none
VL  - 47
JF  - Journal of Medical Systems
N2  - 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.
TI  - Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight
A1  - Ferreras, Antonio
A1  - Sumalla Cano, Sandra
A1  - Martínez-Licort, Rosmeri
A1  - Elío Pascual, Iñaki
A1  - Tutusaus, Kilian
A1  - Prola, Thomas
A1  - Vidal Mazón, Juan Luis
A1  - Sahelices, Benjamín
A1  - de la Torre Díez, Isabel
SN  - 1573-689X
ID  - uninimx5420
UR  - http://doi.org/10.1007/s10916-022-01904-1
ER  -