Background 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. Objective 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. Methods 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. Results According to the results, while predicting the exact quantity of vitamins and minerals is shown to be challenging, with regression R2 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). Conclusions 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.

BACKGROUND Micronutrient deficiencies are a global health issue, with over two billion people suffering from deficiencies in vitamins and minerals. Food labels can help improve diets by providing information about the nutritional content of foods to consumers. However, food labels often only include a limited amount of information due to size and readability constraints and may not provide information about all micronutrients. OBJECTIVE This paper aims to examine the possibility of using machine learning algorithms to predict unreported micronutrients from the existing food label data. If unreported micronutrients can be accurately predicted from existing food labels using predictive models, such models can be integrated into mobile applications to provide consumers with additional micronutrient information about foods and help them make more informed diet decisions. METHODS Nutrition data from a total of 5,624 foods are used to train a wide array of machine learning algorithms to predict unreported vitamins and minerals from existing food label data. Models are evaluated using repeated cross-validations to ensure that they are not overfitting. RESULTS According to the results, while predicting the exact quantity of vitamins and minerals is challenging with regression R-squared varied in a wide range from 0.28 (for magnesium) to 0.92 (for manganese), the classification models could 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). CONCLUSIONS The feasibility of predicting unreported micronutrients from existing food labels is demonstrated, for the first time, in this study. The viability of this approach is shown to have the potential to significantly improve consumer knowledge about the micronutrient content of the foods they consume. When integrated into mobile applications, this capability can be made more accessible and engaging for consumers. The implications of these findings for public health are significant, highlighting the potential of technology to enhance consumers’ understanding of the micronutrient content of their diet.