Identifying Risk Factors and Predicting Food Security Status using Supervised Machine Learning Techniques

Abstract

In 2018, more than 821 million undernourished people were registered all over the world. Of these, 239 million were in Sub-Saharan Africa. The numbers are particularly high in Ethiopia, Kenya, Somalia, and South Sudan. The determinant factors of food insecurity in Ethiopia are multidimensional encompassing climate change, civil conflicts, natural disasters, and social norms. This study, hence, aims to identify risk factors and predict food security status at household level in North West Ethiopia using supervised machine learning techniques. To this end, a dataset was gathered from the Dabat Health and Demographic Surveillance and statistically interesting risk factors were identified using logistics regression at a threshold level of p<0.05. Three experiments were also conducted using random forest, support vector machine and decision tree (C4.5) to predict food security status at household level and the performance of each model was evaluated using accuracy, precision, recall, and f1- measure. As a result, the C4.5 algorithm is selected as the best appropriate supervised machine learning algorithm with 97.23% of recall, 91.58% of accuracy, 80.97% of f1-measure, and 69.38% of precision. Family size, level of education, age of the household head, number and types of communication media, numbers of livestock, cultivated land size, access to credit, and access to irrigation are some of the risk factors of food security.