在世卫组织全球结核病数据集上使用机器学习模型对结核病发病率和HIVTB共感染的比较研究
Declan I Emegano1,2, Basil B Duwa3,4, A G Usman3
1Operational Research Center in Healthcare, Near East University, Nicosia/TRNC, Mersin 10, 99138, Turkey. declanikechukwu.emegano@neu.edu.tr.
Scientific reports
|April 21, 2025
概括
机器学习模型使用世卫组织数据准确预测结核病 (TB) 发病率和HIV-TB联合感染率. 这些进展支持全球打击结核病和结核病-艾滋病毒共感染的战略.
科学领域:
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
- 机器学习应用 机器学习应用
背景情况:
- 结核病 (TB) 仍然是一个关键的全球卫生挑战,由艾滋病毒的同时感染加剧.
- 同时感染艾滋病毒会增加结核病复发风险和治疗复杂性.
- 准确预测结核病和艾滋病毒-结核病共感染率对于公共卫生干预至关重要.
研究的目的:
- 采用先进的机器学习 (ML) 技术来预测结核病发病率和艾滋病毒-结核病共感染率.
- 利用来自2023年世界卫生组织 (WHO) 全球结核病负担数据库的数据.
- 用关键指标来评估模型性能,例如准确性,精度,回忆,F1分数和ROC-AUC.
主要方法:
- 数据来源于2023年世卫组织全球结核病负担数据库.
- 开发和比较了包括极端梯度增强 (XGB) 和梯度增强 (GB) 在内的机器学习模型.
- 基于估计每10万人的结核病发病率 (E_inc_100k) 和每10万人的HIV阳性结核病发病率 (e_inc_tbhiv_100k) 的绩效评估.
主要成果:
- XGB模型实现了99.7%的准确性,99.80%的精度,99.6%的回忆,99.7%的F1得分和99.7%的ROC-AUC,用于E_inc_100k预测.
- 对于e_inc_tbhiv_100k,GB模型表现出强的性能,准确度为98.58%,精度为98.32%,回忆率为98.73%,F1得分为98.53%,ROC-AUC为98.58%.
- 这两种模型都在预测结核病和艾滋病毒-结核病共感染率方面表现出高效.
结论:
- 该研究成功实施了ML模型,用于预测结核病和HIV-TB联合感染.
- 这些发现与联合国艾滋病规划署和世卫组织结核病终结战略的目标一致.
- 结果表明,针对结核病和艾滋病毒-结核病共感染的公共卫生战略取得了重大进展.
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