A predictive model of inadequate minimum dietary diversity among women with a child under 24 months in ethiopia: a machine learning approach using the 2016 EDHS.

backgroundMothers with inadequate intake of micronutrients are a serious and collective global health issue, especially in poverty stricken areas. However, the available studies in Ethiopia have been usually focused in early childhood nutrition using old statistical methods. The aim of this study is to apply multiple machine learning algorithms to construct a high fidelity predictive model and identify key predictors of Inadequate Minimum Dietary Diversity for Women among Ethiopian mothers with a child under 24 months.

methodsA weighted sample of 3,914 women from the Ethiopian Demographic Health Survey 2016 was utilized to conduct a secondary analysis of data. The outcome variable was dichotomous: Inadequate Minimum Dietary Diversity for Women or Adequate Minimum Dietary Diversity for Women. The data was divided into 20% and 80% in the testing and training respectively. We used R software version 4.5 to apply and test ML algorithms. To deal with the harsh imbalance of classes, the Adaptive Synthetic method was utilized, and robust feature selection was performed by the Boruta algorithm. An entire set of seven machine learning algorithms classifiers was trained and tested (Accuracy, Recall, F1 score, specificity, precision and AUC).

findingsRandom forest algorithm (accuracy = 95.03%, sensitivity = 92.73%, precision = 97.28% F1-score = 94.94% and AUC = 98.34) was the best predictive model since it had better performance metrics on the test set. Rural residence, unprotected source of drink water, poor wealth index, no media exposure, unimproved toilet facility, no education, age, religion, and traditional method of contraceptive were the top factors to predict minimum dietary diversity of women.

conclusionMachine learning models, specifically the Random forest classifier, are well-suited to predict a mother with Minimum Dietary Diversity, which provides a useful decision-supporting tool to the health officials of the populace. The results of the study suggest evidence based guidance, including the necessity of geographically concentrated interventions and the combined programs that can integrate the effects of nutrition education, family planning, and economic empowerment to help reduce the overwhelming socioeconomic and demographic risk factors to advance poor maternal dietary diversity in Ethiopia.