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 their ability to estimate the probability of rabies in animals investigated as part of an Integrated Bite Case Management program (IBCM). To balance our training data, we used Random Oversampling (ROS) and Synthetic Minority Oversampling Technique. We developed a risk stratification framework based on predicted rabies probabilities. XGB performed better at predicting rabies cases than LR. Oversampling strategies enhanced the model sensitivity making them the preferred technique to predict rare events li

SUBMITTER: Keshavamurthy R 

PROVIDER: S-EPMC11519585 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature