利用基于健康,气候和社会经济因素的可解释的人工智能和亚洲 (2000-2022) 传染病的空间分析
Md Siddikur Rahman1, Md Abu Bokkor Shiddik2
1Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh. siddikur@brur.ac.bd.
International journal of health geographics
|December 23, 2025
概括
机器学习和空间分析揭示了亚洲传染病的关键驱动因素. 结果指导针对结核病和疟疾等公共卫生挑战的有针对性的干预措施.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 传染病在亚洲构成了重大的公共卫生挑战,受气候,社会经济和医疗保健因素的影响.
- 尽管取得了进展,但持续的疾病热点需要深入调查.
- 这项研究考察了从2000年到2022年在41个亚洲国家的传染性疾病模式和决定因素.
研究的目的:
- 应用机器学习和空间分析来识别亚洲传染病的模式和决定因素.
- 为了发现疾病的热点和空间差异.
- 为公共卫生战略和干预提供可操作的见解.
主要方法:
- 利用全球数据库 (WHO,CRU TS,WDI,联合国儿童基金会) 对疾病病例,气候变量和医疗保健指标.
- 采用随机森林用于缺失值归算和马哈拉诺比斯距离用于异常值检测.
- 应用XGBoost,随机森林,SHAP,XAI和Gi*空间统计数据进行分析和解释.
主要成果:
- 结核病例减少,但印度和尼泊尔仍然存在热点. 疟疾病例显著下降,孟加拉国和巴基斯坦的持续风险.
- 登革热病例在2019年达到顶峰,目前的热点在马来西亚和缅甸.
- 空间差异与降水,湿度和医疗保健接入差距相关. XGBoost显示出高准确度 (例如,结核病R2=0.91),SHAP将气候因素确定为关键预测因素.
结论:
- 整合机器学习,空间分析和可解释AI (XAI) 有效地识别疾病决定因素.
- 这些发现为加强亚洲的疾病监测和资源分配提供了可操作的见解.
- 该研究支持制定有针对性的公共卫生干预措施,以打击传染病.
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