Corrigendum to: A Machine Learning Approach to the Prediction of Malaria in Under-five Children: Analysis of the 2021 Nigerian Malaria Indicator Survey

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CORRIGENDUM

Corrigendum to: A Machine Learning Approach to the Prediction of Malaria in Under-five Children: Analysis of the 2021 Nigerian Malaria Indicator Survey

Sbongiseni Makhosonke Mthethwa1 , * Open Modal iD Sileshi Fanta Melesse1 iD
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The Open Public Health Journal • 27 Jan 2026 • CORRIGENDUM • DOI: 10.2174/0118749445396163250602601201
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The correction has been applied to the reference of the article, titled “A Machine Learning Approach to the Prediction of Malaria in Under-five Children: Analysis of the 2021 Nigerian Malaria Indicator Survey,” published in “The Open Public Health Journal,” 2025; 18: e18749445396163 [1].

We apologize for any inconvenience caused and appreciate the opportunity to rectify this matter.

The original article can be found online at

https://openpublichealthjournal.com/VOLUME/18/ELOCATOR/e18749445396163/FULLTEXT/

Original:

NB has low sensitivity (47%) and precision (57%), probably because it assumes that features are independent, while KNN also exhibited low sensitivity (42%) and F1-score (45%), which limits its ability to handle complex data or incorrect settings [53-55].

Corrected:

NB has low sensitivity (47%) and precision (57%), probably because it assumes that features are independent, while KNN also exhibited low sensitivity (42%) and F1-score (45%), which limits its ability to handle complex data or incorrect settings [53, 54].

REFERENCE

1
Mthethwa S, Melesse S. A machine learning approach to the prediction of malaria in under-five children: analysis of the 2021 nigerian malaria indicator survey. Open Public Health J 2025; 18: e18749445396163.