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

**Authors:** Pravin Arun Sawant, Sakshi Shantanu Hiralkar, Yogita Purushottam Hulsurkar, Mugdha Sharad Phutane, Uma Satish Mahajan, Abhay Machindra Kudale

PMC · DOI: 10.4178/epih.e2024044 · 2024-04-13

## TL;DR

This study uses machine learning to predict over-the-counter antibiotic use in rural India and identifies key factors driving this behavior.

## Contribution

The study introduces a novel machine learning approach combining XGBoost and Boruta for predicting OTC antibiotic use in rural settings.

## Key findings

- The prevalence of OTC antibiotic use in rural Pune was 35.9%.
- XGBoost+Boruta model achieved an AUROC of 0.934 with 7 key predictors.
- Key factors include antibiotic use for eye complaints and perception of pharmacy convenience.

## Abstract

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.

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.

In rural Pune, the prevalence of OTC antibiotic use was 35.9% (95% CI, 31.6 to 40.5). The perception that buying medicines directly from a medicine shop/pharmacy is useful, using antibiotics for eye-related complaints, more household members consuming antibiotics, and longer duration and higher doses of antibiotic consumption in rural blocks and other social groups were confirmed as important features by the Boruta algorithm. The final model was the XGBoost+Boruta model with 7 predictors (AUROC, 0.934; 95% CI, 0.891 to 0.978; log-loss, 0.279) log-loss.

XGBoost+Boruta, with 7 predictors, was the most accurate model for predicting OTC antibiotic use in rural Pune. Using OTC antibiotics for eye-related complaints, higher consumption of antibiotics and the perception that buying antibiotics directly from a medicine shop/pharmacy is useful were identified as key factors for planning interventions to improve awareness about proper antibiotic use.

## Full-text entities

- **Diseases:** eye-related complaints (MESH:D015817)

## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11417445/full.md

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Source: https://tomesphere.com/paper/PMC11417445