Machine learning algorithms for predicting the determinants of minimum dietary diversity among Bangladeshi children

Abstract

Background: In developing countries, the minimum dietary diversity (MDD) measure of dietary quality is widely used to define the dietary habits of infants between the ages of 6 and 23 months. However, the particular situation in Bangladesh shows that just 34% of kids have access to a food that complies with the bare minimum acceptable norms. The main aim of this study was to predict the determinants of minimum dietary diversity (MDD) among Bangladeshi children. Methods: This study was based on data from the Bangladesh Demographic and Health Survey (2017-2018 BDHS). Statistical analysis involving a χ2 test alongside machine learning (ML) algorithms was employed to identify the factors associated with MDD and to predict the factors influencing MDD outcomes within the context of Bangladesh. Results: The random forest (accuracy =0.854, specificity =0.639, sensitivity =0.927, precision =0.883, F1-score = 0.905, area under the curve: AUC = 0.711) show the best performance than others machine learning model. The random forest model shows the “division”, “mother age”, “wealth index”, “partner education”, “total number of children” and “mother education” play an important role to predict the determinants of MDD in Bangladesh. Conclusions: To enhance newborn and young child feeding practices, it is strongly advised to boost women’s empowerment and mother’s education. To protect the health of infants, government healthcare authorities should implement public education programs and awareness campaigns in addition to enforcing the appropriate laws and regulations.