Machine learning to improve the understanding of rabies epidemiology in low surveillance settings

Machine learning to improve the understanding of rabies epidemiology in low surveillance settings

Abstract In low and middle-income countries, a large proportion of animal rabies investigations end without a conclusive diagnosis leading to epidemiologic interpretations informed by clinical, rather than laboratory data. We compared Extreme Gradient Boosting (XGB) with Logistic Regression (LR) for...

Full description

Saved in:
Bibliographic Details
Main Authors: Ravikiran Keshavamurthy, Cassandra Boutelle, Yoshinori Nakazawa, Haim Joseph, Dady W. Joseph, Pierre Dilius, Andrew D. Gibson, Ryan M. Wallace
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Scientific Reports
Subjects:
Rabies epidemiology
Prediction
Machine learning
Extreme gradient boosting
Risk stratification
Zoonotic disease surveillance
Online Access:https://doi.org/10.1038/s41598-024-76089-3
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items