Addressing class imbalance in lassa fever epidemic data, using machine learning: a case study with SMOTE and random forest

Addressing class imbalance in lassa fever epidemic data, using machine learning: a case study with SMOTE and random forest

Class imbalance in epidemiological datasets, particularly for rare outcomes like Lassa Fever fatalities, complicates predictive modeling. This study addresses the issue by employing SMOTE to rebalance the dataset and Random Forest for classification while identifying significant predictors such as...

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Bibliographic Details
Main Authors: Osowomuabe Njama-Abang, Denis Ashishie, Paul Bukie
Format: Article
Language:English
Published: Nigerian Society of Physical Sciences 2025-08-01
Series:Journal of Nigerian Society of Physical Sciences
Subjects:
Lassa fever
Machine learning
SMOTE
Random forest
Class imbalance
Online Access:https://journal.nsps.org.ng/index.php/jnsps/article/view/2586
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