AUTHOR=Yehuala Tirualem Zeleke , Agimas Muluken Chanie , Derseh Nebiyu Mekonnen , Wubante Sisay Maru , Fente Bezawit Melak , Yismaw Getaneh Awoke , Tesfie Tigabu Kidie 

TITLE=Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa

JOURNAL=Frontiers in Public Health

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1362392

DOI=10.3389/fpubh.2024.1362392

ISSN=2296-2565

ABSTRACT=Background: Acute respiratory infections (ARI) are the leading cause of death in children under five globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed at predicting absence of maternal healthcare-seeking behavior and identifying its associated factors among under-five children in sub-Saharan Africa (SSA).
Methods: The sub-Saharan African countries’ demographic health survey was the source of the data set.  We used a weighted sample of 16,832 under-five children in this study. The data was processed using Python (version 3.9), and machine learning models like Extreme Gradient Boosting (XGB), Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes were applied. In this study, we used evaluation metrics, including the AUC ROC curve, accuracy, precision, recall, and F-measure to assess the performance of the predictive models.
Result: In this study, a weighted sample of 16,832 under-five children was used in the final analysis. Among the proposed machine learning models, the Random Forest was the best predicted model with an accuracy of 88.89%, a precision of 89.5%, an F-measure of 83%, an AUC ROC curve of 95.8%, and a recall of 77.6% in predicting the absence of mothers’ health-seeking behavior on the ARI. The accuracy for naïve bays was the lowest (66.41%), when compared to other proposed models. No media exposure, living in rural areas, not breastfeeding, poor wealth status, home delivery, no ANC visit, no maternal education, mothers’ age group of 35–49 years, and distance to health facility were significant predictors for the absence of mothers’ health-seeking behaviors on ARI. On the other hand, undernourished children like stunting, underweight, and wasting status, having diarrhea, birth size, married women, being a male or female sex child, and having an occupation for maternal were significantly associated with good mothers health seeking behaviors on ARIs among under-five children.
Conclusion: The random forest model provides greater predictive power for estimating mother’s health seeking behaviors on ARI risk factors.  This leads to a recommendation for policy direction to reduce children mortality by ARI in Sub Saharan countries.