Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province.

ABSTRACT:

Background

Bacterial dysentery (BD) is a leading cause of diarrhea-related mortality globally, with its incidence heavily influenced by environmental factors. However, a climate zone-specific predictive model for BD was currently lacking in Sichuan Province.

Objective

This study aims to employ interpretable machine learning to explore the influence of environmental factors on BD incidence across different climate zones and to elucidate their interaction mechanisms.

Methods

Monthly data on meteorological and ecological factors, along with BD case reports, were collected from 183 counties in Sichuan Province (2005-2023). The eXtreme Gradient Boosting (XGBoost) algorithm was employed to assess the influence of key environmental features, including precipitation, temperat

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12307499 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature