Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods.

ABSTRACT:

Objectives

Over-the-counter (OTC) antibiotic use can cause antibiotic resistance, threatening global public health gains. To counter OTC use, this study used machine learning (ML) methods to identify predictors of OTC antibiotic use in rural Pune, India.

Methods

The features of OTC antibiotic use were selected using stepwise logistic, lasso, random forest, XGBoost, and Boruta algorithms. Regression and tree-based models with all confirmed and tentatively important features were built to predict the use of OTC antibiotics. Five-fold cross-validation was used to tune the models' hyperparameters. The final model was selected based on the highest area under the curve (AUROC) with a 95% confidence interval (CI) and the lowest log-loss.

Results

In rural Pune, the prevalence

SUBMITTER: Sawant PA 

PROVIDER: S-EPMC11417445 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature