在医疗领域的机器学习中使用的处理不平衡数据的方法:关于查加斯病数据库预测情况的案例研究
André G Coimbra1,2, Cleiane G Oliveira2,3, Matheus P Libório1
1Graduate Program in Computer Modeling and Systems, UNIMONTES, Montes Claros, Brazil.
PloS one
|May 13, 2025
概括
在机器学习中处理不平衡的数据对于医疗保健算法至关重要. 这项研究比较了中风预测方法,并引入了查加斯病的新方法,旨在改善患者的治疗结果和资源配置.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 在医疗保健中越来越多地用于风险管理和诊断等任务.
- 医疗保健的ML的一个关键挑战是有效地处理不平衡的数据集.
- 不平衡的数据可能会对患者监测和预后中使用的分类算法的性能产生重大影响.
研究的目的:
- 在医疗保健中对处理不平衡数据的各种技术进行比较分析.
- 为了评估这些技术的有效性,当与预测中风的分类算法相结合时.
- 提出和应用一种新的方法,使用粒子集群优化 (PSO) 和天真贝叶斯对查加斯病的预测.
主要方法:
- 对不平衡的数据处理技术进行比较分析.
- 用不同的数据平衡策略评估分类算法的性能.
- 开发和应用混合粒子集群优化 (PSO) 和天真贝叶斯模型.
主要成果:
- 在中风预测中不同不平衡数据技术的比较性能指标.
- 展示拟议的PSO-Naive Bayes方法对查加斯病数据的有效性.
- 识别最佳策略,以改善ML模型准确性与不平衡的医疗保健数据.
结论:
- 对不平衡数据的有效处理对于医疗保健中可靠的ML应用至关重要.
- 拟议的PSO-Naive Bayes方法显示了改善疾病预测准确性的承诺.
- 这些进步可以带来更好的患者护理,降低成本和高效的资源管理.
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