eprintid: 5420 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/54/20 datestamp: 2023-01-13 23:30:04 lastmod: 2023-01-13 23:30:04 status_changed: 2023-01-13 23:30:04 type: article metadata_visibility: show creators_name: Ferreras, Antonio creators_name: Sumalla Cano, Sandra creators_name: Martínez-Licort, Rosmeri creators_name: Elío Pascual, Iñaki creators_name: Tutusaus, Kilian creators_name: Prola, Thomas creators_name: Vidal Mazón, Juan Luis creators_name: Sahelices, Benjamín creators_name: de la Torre Díez, Isabel creators_id: creators_id: sandra.sumalla@uneatlantico.es creators_id: creators_id: inaki.elio@uneatlantico.es creators_id: kilian.tutusaus@uneatlantico.es creators_id: thomas.prola@uneatlantico.es creators_id: juanluis.vidal@uneatlantico.es creators_id: creators_id: title: Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight ispublished: pub subjects: uneat_bm subjects: uneat_eng subjects: uneat_sn divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: none keywords: Nutrition; Health; Machine learning; Obesity; Overweight 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. date: 2023 publication: Journal of Medical Systems volume: 47 number: 1 id_number: doi:10.1007/s10916-022-01904-1 refereed: TRUE issn: 1573-689X official_url: http://doi.org/10.1007/s10916-022-01904-1 access: close language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Cerrado Inglés 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. metadata Ferreras, Antonio; Sumalla Cano, Sandra; Martínez-Licort, Rosmeri; Elío Pascual, Iñaki; Tutusaus, Kilian; Prola, Thomas; Vidal Mazón, Juan Luis; Sahelices, Benjamín y de la Torre Díez, Isabel mail SIN ESPECIFICAR, sandra.sumalla@uneatlantico.es, SIN ESPECIFICAR, inaki.elio@uneatlantico.es, kilian.tutusaus@uneatlantico.es, thomas.prola@uneatlantico.es, juanluis.vidal@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight. Journal of Medical Systems, 47 (1). ISSN 1573-689X