Food security prediction from heterogeneous data combining machine and deep learning methods

Hugo Deléglise, Roberto Interdonato, Agnès Bégué, Elodie Maître d'Hôtel, Maguelonne Teisseire, Mathieu Roche

Abstract: Highlights•The analysis of Food Security related phenomena poses several research challenges.•We focus on the Food Consumption Score and Household Dietary Diversity Score indicators.•We propose the FSPHD machine learning framework for the prediction of such indicators.•We use a large set of input data heterogeneous in terms of format and domain.•The results show promising performances that outperform competing methods.