Associating Measles Vaccine Uptake Classification and its Underlying Factors Using an Ensemble of Machine Learning Models

Measles is one of the significant public health issues responsible for the high mortality rate around the globe, especially for developing countries. Using nationally representative demographic and health survey data, measles vaccine utilization has been classified, and its underlying factors are identified through an ensemble Machine Learning (ML) approach. Firstly, missing values are imputed employing various approaches, and then several feature selection techniques have been applied to identify the crucial attributes for predicting measles vaccination. A grid search hyperparameter optimization technique has been applied for tuning the critical hyperparameters of different ML models, such as Naive Bayes, random forest, decision tree, XGboost, and lightgbm. The categorization performance of the individual optimized ML model as all as their ensembles have been…