<p>Background and rationale: Global context provided, but regional and local context of Rwanda on stunting alluded but not quantified. There is need to quantify that aspect. Importance of machine learning is well articulated as well as rationale of its use to predict stunting and aid treatment is well articulated.</p><p>Methods: The methodology is not clear in terms of definition of terms, type of information included from the DHIS data, and data management and analysis not well articulated. How the models were created including logistics regression and how the OR were calculated including the cut off to consider a factor significant is not articulated.</p><p>Results: Quantity of data analyzed not indicated, 95% CI for the OR not indicated making it difficult to interpret the OR. The sentence "The test accuracy, sensitivity, specificity, and F1 were calculated using the confusion matrix" is a methodology but its included in the results section&nbsp;</p><p>Conclusion: Conclusion not well linked to the results, interpretation and public health importance of ML globally and in local context is clear.&nbsp;</p><p>NB: ML is an interesting topic and its application in public health interventions is increasing. With improvement on clarity, it will be interesting to present.</p>