An Automatic Determining Food Security Status: Machine Learning based Analysis of Household Survey Data

• Main Authors: Abdul Razzaq, Umar Ijaz Ahmed, Sarfraz Hashim, Aamir Hussain, Salman Qadri, Sami Ullah, Ali Nawaz Shah, Ali Imran, Attika Asghar
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: International Journal of Food Properties
• Subjects: food security; machine learning; mobile app; householders; survey; random forest
• Online Access: http://dx.doi.org/10.1080/10942912.2021.1919703
• ISSN: 1094-2912; 1532-2386

Household food security is a major issue in developing countries like Pakistan. Despite significant breakthroughs in grain production within the country, the problem of food availability and utilization persists. Diet is one of the most potent determinants of nutritional condition. The dietary intake method has been utilized to determine the food security status of households, which depends on various factors. There are no automatic and user-friendly methods available to decide food security status, which is generally determined by manually calculating calorie intakes. Due to its high performance and precision, machine learning holds major significance. In this paper, the status of food security has been examined by applying machine learning algorithms, namely, support vector machine, naïve Bayes, k-nearest neighbors, random forest, logistic regression, and neural network, on survey data of households for best predicting the status. A food analysis (FA) app has been developed to automatically predict the FAO status of a household’s food security by implementing the random forest model that found higher precision among algorithms. Additionally, the proposed mobile app will also be helpful for collecting the households’ data. Furthermore, the objective of the study was to enhance food security awareness among individuals.