Machine Learning-based predictive model for the prognosis of human papillomavirus (HPV) vaccination attrition

Abstract

Currently, one of the diseases that is causing a large number of deaths in Peru is cervical cancer caused by the human papillomavirus (HPV). However, the application of the vaccine against this disease can protect against certain strains of HPV. The study consisted of the development of a predictive model using Machine Learning for the prognosis of HPV vaccination attrition in girls between 9 and 13 years of age. The data used came from the "HPV vaccination system" of the Peruvian Ministry of Health (MINSA). The methodology consisted of developing four supervised learning models: Decision Tree Classifier, Random Forest Classifier, Extra Trees Classifier and Extreme Gradient Boosting with the intention of comparing the results and choosing the best performing model for its respective calibration and to be used through a graphical interface. The results showed that the best learning model was Random Forest Classifier, with an Accuracy Score of 63.6140%, AUC of 63.6183%, Recall of 63% and F1-score of 63%; which indicates that the model classifies 64% of the cases as girls who drop out of the HPV vaccination program.