通过混合机器学习模型和灵敏度分析,提高智能医疗系统的预测准确度
1School of Health Services and Management, Hubei Preschool Teachers College, Wuhan, China.
Computer methods in biomechanics and biomedical engineering
|November 12, 2025
概括
这项研究引入了针对智能医疗保健的优化机器学习模型,提高了疾病预测的准确性. 像STPD这样的混合模型实现了卓越的性能,通过可靠的预测有望改善患者的治疗结果.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于预测分析.
- 计算健康信息学 计算健康信息学
背景情况:
- 智能健康系统利用机器学习 (ML) 和电子健康记录 (EHR) 进行疾病预测和个性化护理.
- 现有的ML模型需要优化,以提高处理大型医疗数据集的准确性.
- 有效的预测对于及时干预和改善患者管理至关重要.
研究的目的:
- 提出和评估用于智能医疗保健的新型混合机器学习模型.
- 优化堆叠分类器 (StackingC) 和袋装分类器 (BaggingC) 使用草原狗优化 (PDO) 和灰天优化算法 (STOA).
- 评估优化模型的预测准确度和概括能力.
主要方法:
- 混合模型的开发,将堆叠C和包装C与PDO和STOA优化相结合.
- 使用优化算法进行属性选择和模型精度最大化.
- 对风险预测一致性的最佳混合模型 (STPD和STST) 的灵敏度分析.
主要成果:
- 与袋装C (0.958) 相比,堆叠C显示出更高的整体准确性 (0.979).
- 优化的混合模型STPD和STST实现了高整体精度 (分别为0.985和0.980).
- 在测试数据 (0.942) 上,StackingC 显示出更好的概括性能,尽管 BaggingC 的训练精度更高 (0.961).
结论:
- 混合ML方法,优化PDO和STOA,显著提高预测分析在智能医疗保健.
- 拟议的模型提供了准确和可靠的疾病预测,有助于改善患者的治疗结果.
- 敏感性分析证实了这些混合模型的风险预测一致性和可靠性.
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