An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors

An XGBoost Approach to Predictive Modelling of Rift Valley Fever Outbreaks in Kenya Using Climatic Factors

Reports of Rift Valley fever (RVF), a highly climate-sensitive zoonotic disease, have been rather frequent in Kenya. Although multiple empirical analyses have shown that machine learning methods outperform time series models in forecasting time series data, there is limited evidence of their applica...

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Bibliographic Details
Main Authors: Damaris Mulwa, Benedicto Kazuzuru, Gerald Misinzo, Benard Bett
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Big Data and Cognitive Computing
Subjects:
machine learning models
XGBoost
accuracy
geo-climatic variables
AUC/ROC curves
Online Access:https://www.mdpi.com/2504-2289/8/11/148
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