Rahman Md Siddikur, Shiddik Md Abu Bokkor
Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh.
Sci Rep. 2025 Aug 4;15(1):28334. doi: 10.1038/s41598-025-12872-0.
Malaria remains a significant global health concern, contributing to substantial morbidity and mortality worldwide. To inform efforts aimed at alleviating the global malaria burden, this study utilized spatial analysis, advanced machine learning (ML), and explainable AI (XAI) to identify high-risk areas, uncover key determinants, predict disease outcomes, and establish causal relationships. This study analyzed data from 106 countries between 2000 and 2022, sourced from the World Health Organization, World Bank and UNICEF. A high-performance ML classifier, XGBoost, combined with XAI and causal AI (CAI) techniques was employed to evaluate malaria incidence and mortality. Spatial autocorrelation analyses, such as Getis-Ord Gi* and Moran's I, were utilized to detect significant geographical clusters and hotspots of malaria. In 2022, malaria cases reached 251.75 million, while the peak of malaria-related fatalities occurred in 2020, totaling 99,554. Nigeria recorded the highest malaria incidence (1,332.99 million), followed by the Democratic Republic of the Congo (623.16 million) and India (319.83 million). South Sudan (149,753), Zambia (143,546), and the Central African Republic (124,801) exhibited the highest malaria mortality rates. High-incidence clusters were observed in Benin, Burkina Faso, and Ghana, with substantial mortality clusters in Benin, the Central African Republic, and Liberia. The XGBoost model demonstrated the best predictive performance for malaria incidence and mortality (RMSE = 0.63, r² = 0.93, adjusted r² = 0.92, and MAE = 0.46). The XAI and CAI methodologies identified key determinants of malaria, such as access to basic sanitation, electricity availability, population growth, and the under-5 mortality rate. Our integrated framework, driven by machine learning and artificial intelligence, offers actionable insights for identifying determinants and hotspots of malaria through spatial analysis. The study advocates for the incorporation of AI-driven spatial models into national malaria surveillance systems to facilitate evidence-based and targeted interventions.
疟疾仍然是全球重大的健康问题，在全球范围内导致大量发病和死亡。为了为减轻全球疟疾负担的努力提供信息，本研究利用空间分析、先进的机器学习（ML）和可解释人工智能（XAI）来识别高风险地区、揭示关键决定因素、预测疾病结果并建立因果关系。本研究分析了2000年至2022年期间来自106个国家的数据，数据来源为世界卫生组织、世界银行和联合国儿童基金会。采用了一种高性能的ML分类器XGBoost，并结合XAI和因果人工智能（CAI）技术来评估疟疾发病率和死亡率。利用空间自相关分析，如Getis-Ord Gi*和莫兰指数（Moran's I），来检测疟疾的显著地理聚集区和热点地区。2022年，疟疾病例达到2.5175亿例，而与疟疾相关的死亡人数峰值出现在2020年，共计99554人。尼日利亚的疟疾发病率最高（13.3299亿例），其次是刚果民主共和国（6.2316亿例）和印度（3.1983亿例）。南苏丹（149753例）、赞比亚（143546例）和中非共和国（124801例）的疟疾死亡率最高。在贝宁、布基纳法索和加纳观察到高发病聚集区，在贝宁、中非共和国和利比里亚观察到高死亡率聚集区。XGBoost模型在疟疾发病率和死亡率方面表现出最佳的预测性能（均方根误差RMSE = 0.63，决定系数r² = 0.93，调整后的决定系数adjusted r² = 0.92，平均绝对误差MAE = 0.46）。XAI和CAI方法确定了疟疾的关键决定因素，如基本卫生设施的可及性、电力供应、人口增长和五岁以下儿童死亡率。我们由机器学习和人工智能驱动的综合框架，通过空间分析为识别疟疾的决定因素和热点地区提供了可操作的见解。该研究主张将人工智能驱动的空间模型纳入国家疟疾监测系统，以促进基于证据的针对性干预措施。
