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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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相关实验视频

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基于可靠的技术在高维数据中选择最佳特征:适用于不同的健康数据库.

Ibrar Hussain1, Moiz Qureshi2,3, Muhammad Ismail4,3

  • 1Department of Statistics Abdul Wali Khan University Mardan, Pakistan.

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概括

这项研究引入了一种混合基因选择方法,将信号与噪声比和情绪中位测试相结合,用于高维生物信息学数据. 该方法有效地识别了关键基因,提高了分类准确性,并减少了随机森林和KNN模型中的错误.

关键词:
高维数据是高维数据.混合技术是一种混合技术.机器学习模型的机器学习模型情绪中位测试 情绪中位测试优化基因选择的优化单一的噪音比分得分是单一的.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 统计遗传学 统计遗传学

背景情况:

  • 生物信息学中的高维数据,特别是来自微阵列的数据,由于大量的基因和很少的样本,提出了挑战.
  • 冗余的基因掩盖了重要的生物信号,使准确的分类复杂化,并增加了概括错误.
  • 有效的基因选择对于减少维度和提高机器学习模型性能至关重要.

研究的目的:

  • 开发和评估一种用于高维生物数据的新型混合基因选择方法.
  • 将信号与噪声比 (SNR) 与情绪中位测试相结合,以实现可靠的基因识别.
  • 用随机森林和KNN来评估所选基因在提高分类准确性的有效性.

主要方法:

  • 一种混合基因选择策略,结合信号与噪声比 (SNR) 评分和情绪中位测试.
  • 使用Mood中位测试来处理非正常或偏差的数据,并识别显著的基因变化.
  • 通过将SNR值除以基因的显著P值,从心情中位测试中计算出一个新的"Md分数".

主要成果:

  • 混合方法成功地确定了具有高分类重要性和低噪音的重要基因.
  • 使用随机森林和K-最近邻居 (KNN) 与选定的基因进行分类,证明了更好的准确性.
  • 与传统的基因选择技术相比,提出的方法在高维数据集上实现了较低的分类错误率.

结论:

  • 混合基因选择方法为高维生物信息学数据提供了强大而有效的解决方案.
  • 这种方法通过选择生物学上相关和统计学上显著的基因来提高分类性能.
  • 这些发现表明,这种技术是改善复杂生物分析中基因选择的宝贵工具.