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Erschienen in: Wiener klinische Wochenschrift 13-14/2021

18.09.2020 | original article

A data-driven framework for introducing predictive analytics into expanded program on immunization in Pakistan

verfasst von: Sadaf Qazi, Muhammad Usman, Azhar Mahmood

Erschienen in: Wiener klinische Wochenschrift | Ausgabe 13-14/2021

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Summary

Background

Pakistan has a nationwide expanded program on immunization (EPI), yet vaccination coverage in Pakistan is quite low. Recently, an analytical model has been proposed to improve the coverage by identifying children who are most likely to miss any of the vaccines included in the immunization schedule, known as defaulters; however, a number of limitations remain unresolved in the previously proposed model. Firstly, it only classified children into two stages: defaulters and non-defaulters, considering all children at high risk of defaulting even if only one dose is missed. Secondly, there was no categorisation of high and low coverage areas for prioritised vaccination. The aim of this study was to propose a prediction framework for the accurate identification of defaulters.

Methods

We have utilised a sample dataset extracted from the Pakistan Demographic and Health Survey (PDHS, 2017–2018). This contained 7153 data records with 19 demographic and socioeconomic attributes, which were used for defaulter prediction and the identification of association rules to understand the relation between demographics of the child and the vaccination status.

Results

Using a multilayer perceptron (MLP) classifier, the proposed model achieved 98% accuracy and 0.994 for the area under the curve (AUC), to correctly identify the children who are likely to default from immunization series at different risk stages.

Conclusion

The proposed framework in this study is a step forward towards a data-driven approach and provides a set of machine learning techniques to utilise predictive analytics. Hence, this can reinforce immunization programs by expediting targeted action to reduce drop-outs.
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Metadaten
Titel
A data-driven framework for introducing predictive analytics into expanded program on immunization in Pakistan
verfasst von
Sadaf Qazi
Muhammad Usman
Azhar Mahmood
Publikationsdatum
18.09.2020
Verlag
Springer Vienna
Erschienen in
Wiener klinische Wochenschrift / Ausgabe 13-14/2021
Print ISSN: 0043-5325
Elektronische ISSN: 1613-7671
DOI
https://doi.org/10.1007/s00508-020-01737-3