رویکردهای مبتنی بر هوش مصنوعی در ارزیابی الگوهای غذایی: یک مرور نظام مند
کد: G-1016
نویسندگان: Hanieh Malmir * ℗, Somayeh Hosseinpour-Niazi, Parvin Mirmiran
زمان بندی: زمان بندی نشده!
برچسب: دستیار مجازی هوشمند
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background and aims: The development of artificial intelligence has provided new opportunities for research in the field of nutrition science. This article was done with the aim of reviewing and comprehensively examining studies related to the field of diet and food patterns that have used artificial intelligence techniques and machine learning algorithms. Methods: All studies published until June 2023 were searched using PubMed Cochrane, EMBASE and SCOPUS databases and with related keywords. No time and language restrictions were applied during the search. Results: After a complete review of the articles, 31 relevant articles were selected that were consistent with the purpose of the present study. Different machine learning methods have different accuracy in predicting food patterns. For example, the intelligent neural network is more accurate in predicting healthy food index quintiles, while it is more accurate in decision tree meals. Another application of machine learning is extracting food patterns and investigating their relationship with various diseases such as obesity, heart disease, stroke, risk of death from cardiovascular disease and cancer. Also, some machine learning methods, such as decision trees, are able to provide models for predicting adherence to different diets, such as the Mediterranean diet. Conclusion: Different artificial intelligence methods can help to better understand food patterns related to chronic diseases. The most important algorithms in the study of food patterns are decision tree, random forest, K-means, K-nearest neighbor, regression methods, support vector machine and intelligent neural network. These methods can help to better understand dietary patterns associated with chronic diseases by categorizing and finding hidden associations between groups and foods. More studies are needed in this area to better understand these connections.
کلمات کلیدی
AI, Machine learning, Nutrition, Food pattern
