Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain.
Research Group on Foods, Nutritional Biochemistry and Health, European University of the Atlantic, Santander, 39011, Spain.
J Med Syst. 2023 Jan 13;47(1):8. doi: 10.1007/s10916-022-01904-1.
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.
肥胖和超重问题在过去一年中有所增加，已成为一种流行疾病，其产生的原因是久坐不动的生活方式和富含糖、精制淀粉、脂肪和卡路里的不健康饮食。机器学习（ML）已被证明在科学界非常有用，尤其是在卫生领域。为了向营养师和饮食学家提供有用的工具，研究人员致力于开发 ML 和深度学习（DL）算法和模型，并在文献中进行了相关搜索。我们使用了系统评价和荟萃分析的首选报告项目（PRISMA）方案，这是一种非常常见的用于开展综述的技术。在我们的提案中，筛选出了 17 篇文章，这些文章将 ML 和 DL 应用于疾病预测、治疗策略制定、个性化营养改善等领域。尽管人们期望使用 DL 可以获得更好的结果，但根据所选调查，传统方法仍然是最常用的方法，而且在这两种情况下的收益都在积极数值附近波动，这在很大程度上取决于所使用的人工智能范例，而不是数据库（在每种情况下都进行了转换）。结论：为该领域的文献提供了重要的汇编。ML 模型在清理数据方面耗时较长，但（与 DL 一样）它们允许对大量数据进行自动建模，这使它们优于传统统计学。
