Abstract

As childhood vaccination is vital for children to prevent them from vaccine-preventable diseases, vaccine hesitancy (VH) is a phenomenon that can jeopardise this preventive mechanism. This study aims to develop an instrument to predict VH among parents towards childhood immunization by using machine learning (ML) algorithms. In this study, the approach of predicting VH was to focus on attitude, behaviour, and practice through the administration of a questionnaire, which was verified by statistical analysis and ML algorithms. The researchers developed a 26-item instrument adapted from two other studies. Experts from three different fields reviewed the instrument for content validity. From the pilot study, a 13-item instrument was generated and has a Cronbach alpha value of 0.850 for reliability. The instrument was applied to 510 respondents who are parents attending the Obstetrics and Gynaecology and Pediatric Clinics of the state referral hospital, and have children between the ages of 0 and 15 years old. The data collected was subjected to 10 ML algorithms. It was found that, in terms of accuracy, logistic regression with bagging produced the best results, with 99.02% on the hold-out set and 97.45% on the 10-fold cross-validation set. The results of our study indicate that the instrument has potential to anticipate parental VH in the local context. The instrument’s prospects can be further enhanced if its performance is validated against an objective parameter such as vaccination records.

Key words: vaccine hesitancy, childhood, vaccination, immunization, machine learning