AI Article Synopsis
- - Obesity and overweight rates have surged recently, largely due to inactive lifestyles and poor diets high in sugars and fats, prompting research into machine learning (ML) and deep learning (DL) as potential solutions in nutrition and health.
- - A systematic review using the PRISMA protocol identified 17 studies that apply ML and DL for predicting health issues, creating treatment plans, and enhancing personalized nutrition, although traditional methods remain more commonly used.
- - The review highlights that while ML and DL models can efficiently handle large datasets, they require significant time to prepare data and that their effectiveness is often influenced more by the quality of data rather than the AI techniques used.
Article 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.
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| http://dx.doi.org/10.1007/s10916-022-01904-1 | DOI Listing |
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