@Article{cmc.2020.012507,
AUTHOR = {Sadaf Qazi, Muhammad Usman, Azhar Mahmood, Aaqif Afzaal Abbasi, Muhammad Attique, Yunyoung Nam},
TITLE = {Smart Healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {589--602},
URL = {http://www.techscience.com/cmc/v66n1/40467},
ISSN = {1546-2226},
ABSTRACT = {Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases, child mortality and morbidity. Expanded Program
on Immunization (EPI) is a nation-wide program in Pakistan to implement immunization activities, however the coverage is quite low despite the accessibility of
free vaccination. This study proposes a defaulter prediction model for accurate
identification of defaulters. Our proposed framework classifies defaulters at five
different stages: defaulter, partially high, partially medium, partially low, and
unvaccinated to reinforce targeted interventions by accurately predicting children
at high risk of defaulting from the immunization schedule. Different machine
learning algorithms are applied on Pakistan Demographic and Health Survey
(2017–18) dataset. Multilayer Perceptron yielded 98.5% accuracy for correctly
identifying children who are likely to default from immunization series at different risk stages of being defaulter. In this paper, the proposed defaulters’ prediction
framework is a step forward towards a data-driven approach and provides a set of
machine learning techniques to take advantage of predictive analytics. Hence, predictive analytics can reinforce immunization programs by expediting targeted
action to reduce dropouts. Specially, the accurate predictions support targeted
messages sent to at-risk parents’ and caretakers’ consumer devices (e.g., smartphones) to maximize healthcare outcomes.},
DOI = {10.32604/cmc.2020.012507}
}