Abstract
Assessing nutritional content is very relevant for patients suffering from various diseases, professional athletes, and for health reasons is becoming part of everyday life for many. However, it is a very challenging task as it requires complete and reliable sources. We introduce a machine learning pipeline for predicting macronutrient values of foods using learned vector representations from short text descriptions of food products. On a dataset used from health specialists, containing short descriptions of foods and macronutrient values: we generate paragraph embeddings, introduce clustering in food groups, using graph-based vector representations, that include food domain knowledge information, and train regression models for each cluster. The predictions are for four macronutrients: carbohydrates, fat, protein and water. The highest accuracy was obtained for carbohydrate predictions – 86%, compared to the baseline – 27% and 36%. The protein predictions yielded the best results across all clusters, 53%–77% of the values fall in the tolerance-level range. These results were obtained using short descriptions, the embeddings can be improved if they are learned on longer descriptions, which would lead to better prediction results. Since the task of calculating macronutrients requires exact quantities of ingredients, these results obtained only from short description are a huge leap forward.
Highlights
There is no denying that nutrition has become a core factor to today’s society, and an undeniable solution to the global health-crisis [1,2,3,4]
Our dataset for evaluation is a subset from the original dataset, obtained by extracting the English food product descriptions, alongside the columns with the macronutrient values
If we do not consider the results from these two clusters, the best results are obtained for protein predictions in cluster 4 (70%–72%) and fat predictions (66%–68%), but compared to the baseline median of that cluster, they are not much better, but if we look at the results from the protein predictions in cluster 8 (60%–67%) we can see that the obtained accuracies are much higher than the baseline mean and median for this cluster
Summary
There is no denying that nutrition has become a core factor to today’s society, and an undeniable solution to the global health-crisis [1,2,3,4]. We live in a time of a global epidemic of obesity, of diabetes, of inactivity, all connected to bad dietary habits. Many chronic diseases such as high blood pressure, cardiovascular disease, diabetes, some cancers [5], and bone-health diseases are linked to, again – poor dietary habits [6]. Dietary assessment is essential for patients suffering from many diseases (especially diet and nutrition related ones), it is very much needed for professional athletes, and because of the accessibility of meal tracking mobile applications it is becoming part of everyday habits of a vast majority of individuals, for health, fitness, or weight loss/gain. Nutritional epidemiologists are raising concern about micronutrients like – sodium, whose intake should be monitored for individuals suffering from
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