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

作者：Yao Zhang, Qiaolin Wang, Wei Peng, Mengyuan Zhang, Yao Qin, Lun Zhang, Rongjie Wei, Dianju Kang · 发表于：Frontiers in Public Health · 年份：2025 · DOI：10.3389/fpubh.2025.1598247 · 被引用次数：5 · 研究领域：Clostridium difficile and Clostridium perfringens research、Gut microbiota and health、Child Nutrition and Water Access

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, temperature, PM10, potential evaporation, vegetation cover, and NDVI, on BD incidence. To enhance interpretability, the model's outputs were visualized and explained using SHapley Additive Explanations (SHAP). Results: A machine learning model was developed to assess the impact of environmental factors on BD incidence across different climate zones. The findings revealed significant spatial heterogeneity in key drivers of BD. In the Central Subtropical Humid Climate Zone, BD incidence was predominantly influenced by average temperature, PM10, and minimum temperature. In the Subtropical Semi-Humid Climate Zone, potential evaporation, PM10, and precipitation emerged as the primary determinants. In the Plateau Cold Clima...