Evaluating of machine learning models in predicting COVID-19 vaccine uptake in Kenya

Document Type

Article

Department

Brain and Mind Institute; Internal Medicine (East Africa)

Abstract

Background: Machine learning algorithms are employed to create prognostic and diagnostic models, aiding in public health decision-making and intervention strategies. This study aimed to explore predictive models for the COVID-19 vaccination uptake using machine learning algorithms.

Methods: Supervised machine learning algorithms were utilised to predict accuracy. We employed an evaluation framework to assess the performance of random forest (RF), support vector machine (SVM) and extreme gradient boost (XGB) models in predicting COVID-19 vaccine hesitancy. The data used was based on a cross-sectional study conducted between November 2021 and January 2022 among the general adult public seeking care at six different healthcare facilities in Kenya. The survey, in English, consisted of questions based on demographics, knowledge, and attitudes, including knowledge towards the COVID-19 vaccine. Model effectiveness was evaluated by comparing accuracy, F1 score, sensitivity, specificity and AUROC.

Results: Of the 3996 surveys collected, approximately 68.8% reported being vaccinated with at least one dose. Three models were established using 56 predicting features. Among the three models, RF and XGB yielded better overall performance, with an equal AUROC of 0.88, compared to SVM, which achieved an AUROC of 0.82. Additionally, protection against COVID-19 variables had the highest importance to vaccine uptake, and the following features were healthcare facility and recommendation to the family respectively.

Conclusion: Our findings underscore the potential of leveraging machine learning algorithms in public health strategies to address vaccine hesitancy. Our results indicated that random forest could be a useful predictive tool to identify vaccine uptake which may facilitate effective strategies and further optimize resources. These algorithms were selected for their widespread use, transferability, and proven performance in healthcare prediction tasks, aiming to enhance the predictive accuracy and offer actionable insights into combating vaccine hesitancy.

AKU Student

no

Publication (Name of Journal)

Journal of the Kenya Association of Physicians

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