Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach

Micronutrient deficiencies represent a major global health issue, with over 2 billion individuals experiencing deficiencies in essential vitamins and minerals. Food labels provide consumers with information regarding the nutritional content of food items and have been identified as a potential tool...

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Veröffentlicht in:	Journal of medical Internet research Jg. 25; H. 1; S. e45332
Hauptverfasser:	Razavi, Rouzbeh, Xue, Guisen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Canada Gunther Eysenbach MD MPH, Associate Professor 12.04.2023 JMIR Publications
Schlagworte:	Access Algorithms Classification Consumers Copper Data Data collection Deep learning Descriptive labeling Design Diet Dietary minerals Eating behavior Feasibility Food Food consumption Food Labeling Healthy food Humans Information Information overload Labeling Labels Machine Learning Micronutrients Minerals Mobile Applications Nutrients Nutrition Nutrition research Original Paper Prediction models Public health Regulation Selenium Software Systematic review Tracking Vitamin A Vitamin B1 Vitamin B12 Vitamin B6 Vitamin C Vitamin E Vitamin K Vitamins Zinc United States > US nutrient predictive model mHealth food label machine learning food micronutrient deficiencies nutrition mobile health micronutrient health app predict diet nutrition mobile applications mobile app algorithm
ISSN:	1438-8871, 1439-4456, 1438-8871
Online-Zugang:	Volltext
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