Nutrition information estimation from food photos using machine learning based on multiple datasets

Abstract

Bodyweight, blood pressure, and cholesterol are all risk variables that can aid people in making educated decisions regarding their health promotion activities. Food choices are among the most effective methods for preventing chronic illnesses, including heart disease, diabetes, stroke, and some malignancies. Because various meals give varying amounts of energy and minerals, good eating necessitates keeping track of the nutrients we ingest. Furthermore, there is a paucity of information on whether understanding food constituents might aid in more accurate nutrition calculations. Therefore, this research suggests processing food images on social media to anticipate the contents of each food and extracting nutrition information for each food image to serve as healthy implicit feedback to take advantage of the rapid accumulation of rich photos on social media. The proposed methodology is a framework based on a machine-learning model for predicting food ingredients. We also compute critical health metrics for each ingredient and combine them to obtain nutrition data for the food. The result revealed a promising way of extracting food components and nutrition information. Compared with other researchs, our proposed prediction and attribute extraction strategy achieves a remarkable accuracy of 85%.

Food Classification and Calorie Estimation Using CNN

“…Mustafa Al-Saffar et al [9] "Nutrition information estimation from food photos using machine learning based on multiple datasets" explores the possibility of estimating nutritional details from food pictures using machine learning. The suggested approach entails analyzing social media photos of food to predict what's inside each item and extract nutritional data, offering insightful information for initiatives aimed at promoting health.…”

Predictive analytics of heart disease presence with feature importance based on machine learning algorithms

“…The proposed system starts with data collection and feature selection. The main part is data preprocessing and exploratory data analysis (EDA) [31]. Utilise this to analyse the data and then convert it to the appropriate format.…”

Determination of children's nutritional status with machine learning classification analysis approach

“…Malnutrition cases also provide gaps or opportunities to be attacked by various diseases such as stunting, anemia, and others [3], [4]. This case is also indicated as a result of a lack of information in understanding the causes of malnutrition [5]. So with this it is necessary to have a process of analyzing malnutrition status which is measured based on indicators determining the level of nutritional status [6].…”