Qin Boli, He Rongqing, Qin Xiaopeng, Jiang Jiayan, Zhou Chenxing, Wu Songze, Zhu Jichong, Wu Shaofeng, Chen Jiarui, Xue Jiang, He Kechang, Liu Chong, Ma Jie, Zhan Xinli
The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China.
Xuzhou Medical University, No.209 Tongshan Road, Xuzhou, 221004, Jiangsu, People's Republic of China.
Sci Rep. 2025 Jul 2;15(1):23633. doi: 10.1038/s41598-025-08078-z.
In this study, we investigated the correlation between air pollution indicators and pulmonary tuberculosis (TB) incidence and mortality rates across provincial administrative regions of China from January 2013 to December 2020 to develop predictive models using machine learning. Data on TB rates and six air pollution indicators were collected and analyzed for correlations. Regression models were built using six algorithms, among which the random forest (RF) model showed superior performance. SHapley Additive exPlanations analysis helped interpret the RF model's predictions. Seasonal and lag analyses identified a 10-month optimal lag period. Seasonal autoregressive integrated moving average models were used to predict 2020 TB incidence rates, which were validated by comparing them with actual data. The results indicated significant correlations between air pollution and TB rates, highlighting that air pollution data can predict TB incidence and mortality; therefore, air pollution data can help develop public health strategies. This study emphasized the importance of integrating environmental factors into TB control efforts using artificial intelligence.
在本研究中，我们调查了2013年1月至2020年12月中国省级行政区空气污染指标与肺结核（TB）发病率和死亡率之间的相关性，以使用机器学习开发预测模型。收集了结核病发病率数据和六个空气污染指标并分析其相关性。使用六种算法构建回归模型，其中随机森林（RF）模型表现出卓越性能。SHapley加性解释分析有助于解释RF模型的预测结果。季节性和滞后分析确定了10个月的最佳滞后期。使用季节性自回归积分滑动平均模型预测2020年结核病发病率，并通过与实际数据比较进行验证。结果表明空气污染与结核病发病率之间存在显著相关性，突出表明空气污染数据可预测结核病发病率和死亡率；因此，空气污染数据有助于制定公共卫生策略。本研究强调了利用人工智能将环境因素纳入结核病防控工作的重要性。
