The role of national nutrition programs on stunting reduction among under-two years’ children in Rwanda using machine learning classifiers. Author’s list

Abstract

Background In Rwanda, the prevalence of childhood stunting has slightly decreased over the past five years, from 38% to about 33% today. It is evident whether Rwanda's multi-sectorial approach to reducing child stunting is consistent with the available scientific knowledge. The study was to examine the benefits of national nutrition programs on stunting reduction under two years in Rwanda using ML classifiers. Methods Data from the Rwanda DHS 2015–2020, MEIS and LODA household survey were used. The model was constructed using five algorithms: Support Vector Machine, Logistic Regression, K-Near Neighbor, Random Forest, and Decision Tree. We estimated the hazard ratio for the Cox Proportional Hazard Model and drew the Kaplan-Meier curve to compare the survivor risk of being stunted between program beneficiaries and non-beneficiaries. Precision, recall, F1 score, accuracy, and Area under the Curve (AUC) are the metrics that were used to evaluate each classifier's performance to find the best one. Results Based on the provided data, the study revealed that the ECD program (OR = 0.406, 95 percent CI: 0.172–0.961, p-value = 0.041), NSDS program (OR = 0.463, 95 percent CI: 0.340-