Comparison of Classification Algorithms for Predicting Completeness of Measles Vaccination
Peter Oseghale Ohue ⋅ Oluyemi Adewole Okunlola
Abstract
Supervised machine learning (ML) algorithms are efficient at predicting the occurrence of diseases and they have become more popular as a result of the recent pandemic. A global re-emergence of measles has been reported and with the help of complete vaccination measles can be prevented. An average accuracy score of 0.90 confirms the predictive capacity of ML models. In terms of performance RFC, LDA and LR performed better than CART and KNN.
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