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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Zusammenfassung: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 for improving diets. However, due to governmental regulations and the physical limitations of the labels, food labels often lack comprehensive information about the vitamins and minerals present in foods. As a result, information about most of the micronutrients is absent from existing food labels. This paper aims to examine the possibility of using machine learning algorithms to predict unreported micronutrients such as vitamin A (retinol), vitamin C, vitamin B1 (thiamin), vitamin B2 (riboflavin), vitamin B3 (niacin), vitamin B6, vitamin B12, vitamin E (alpha-tocopherol), vitamin K, and minerals such as magnesium, zinc, phosphorus, selenium, manganese, and copper from nutrition information provided on existing food labels. If unreported micronutrients can be predicted with acceptable accuracies from existing food labels using machine learning predictive models, such models can be integrated into mobile apps to provide consumers with additional micronutrient information about foods and help them make more informed diet decisions. Data from the Food and Nutrient Database for Dietary Studies (FNDDS) data set, representing a total of 5624 foods, were used to train a diverse set of machine learning classification and regression algorithms to predict unreported vitamins and minerals from existing food label data. For each model, hyperparameters were adjusted, and the models were evaluated using repeated cross-validation to ensure that the reported results were not subject to overfitting. According to the results, while predicting the exact quantity of vitamins and minerals is shown to be challenging, with regression R varying in a wide range from 0.28 (for magnesium) to 0.92 (for manganese), the classification models can accurately predict the category ("low," "medium," or "high") level of all minerals and vitamins with accuracies exceeding 0.80. The highest classification accuracies for specific micronutrients are achieved for vitamin B12 (0.94) and phosphorus (0.94), while the lowest are for vitamin E (0.81) and selenium (0.83). This study demonstrates the feasibility of predicting unreported micronutrients from existing food labels using machine learning algorithms. The results show that the approach has the potential to significantly improve consumer knowledge about the micronutrient content of the foods they consume. Integrating these predictive models into mobile apps can enhance their accessibility and engagement with consumers. The implications of this research for public health are noteworthy, underscoring the potential of technology to augment consumers' understanding of the micronutrient content of their diets while also facilitating the tracking of food intake and providing personalized recommendations based on the micronutrient content and individual preferences.
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ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/45332