Machine learning algorithms for prediction of measles one vaccination dropout among 12-23 months children in Ethiopia

Despite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant measles outbreaks, with a nearly fivefold increase in confirmed cases from 2021 to 2023. The WHO has identified being unvaccinated against measles as a major factor driving this resurgence of cases and deaths. Consequently, this study aimed to apply robust machine learning algorithms to predict the key factors contributing to measles vaccination dropout.

This study utilised data from the 2016 Ethiopian Demographic and Health Survey to evaluate measles vaccination dropout. Eight supervised machine learning algorithms were implemented: eXtreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting, Support Vector Machine, Decision Tree, Naïve Bayes, K-Nearest Neighbours and Logistic Regression. Data preprocessing and model development were performed using R language V.4.2.1. The predictive models were evaluated using accuracy, precision, recall, F1-score and area under the curve (AUC). Unlike previous studies, this research utilised Shapley values to interpret individual predictions made by the top-performing machine learning model.

The XGBoost algorithm surpassed all classifiers in predicting measles vaccination dropout (Accuracy and AUC values of 73.9% and 0.813, respectively). The Shapley Beeswarm plot displayed how each feature influenced the best model's predictions. The model predicted that the younger mother's age, religion-Jehovah/Adventist, husband with no and mother with primary education, unemployment of the mother, residence in the Oromia and Somali regions, large family size and older paternal age have a strong positive impact on the measles vaccination dropout.

The measles dropout rate in the country exceeded the recommended threshold of <10%. To tackle this issue, targeted interventions are crucial. Public awareness campaigns, regular health education and partnerships with religious institutions and health extension workers should be implemented, particularly in the identified underprivileged regions. These measures can help reduce measles vaccination dropout rates and enhance overall coverage.