预测先天性梅毒病例:不同机器学习模型的性能评估
Igor Vitor Teixeira1, Morgana Thalita da Silva Leite1, Flávio Leandro de Morais Melo1
1Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil.
PloS one
|June 2, 2023
概括
机器学习模型可以预测先天性梅毒的结果,帮助医疗保健在资源有限的设置. 使用专家选择的特征的AdaBoost模型显示了流行病监测的最佳性能.
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 机器学习
背景情况:
- 性传播感染 (STIs) 构成了全球重大健康和经济挑战,特别是在发展中国家.
- 环境和社会决定因素加剧了像梅毒这样的性传播疾病的传播.
- 机器学习 (ML) 提供了改善传染病流行病学监测的潜力.
研究的目的:
- 评估ML模型来预测不良的先天性梅毒结果.
- 优化医疗保健资源分配和在资源有限的环境中进行干预.
- 加强巴西梅毒的流行病学监测.
主要方法:
- 利用了来自 Pernambuco 的孕妇的临床和社会人口统计数据,巴西的 Mãe Coruja Pernambucana 计划 (PMCP).
- 实施严格的方法,包括特征选择,数据预处理,超参数优化和模型训练/测试.
- 通过三个不同的特征选择技术进行了六项实验.
主要成果:
- 结合卫生专家选择的属性,AdaBoost-BODS-Expert模型表现出卓越的性能.
- 这个模型实现了最佳的评估指标,并获得了PMCP健康专家的认可.
- 结果表明,对于每天对先天性梅毒结果的分类,可靠性很高.
结论:
- ML,特别是AdaBoost-BODS-Expert模型,为预测先天性梅毒结果提供了可靠的工具.
- 该模型的有效性支持其在日常临床使用和流行病学监测中采用.
- 这种方法可以在资源有限的地区显著帮助医疗保健管理.
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