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José R de Morais Filho1, Camilo de L M de Morais2, Anne B F Câmara1

  • 1Biological Chemistry and Chemometrics, Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, RN 5072-970, Brazil.

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概括

一种新的变量选择策略,即一类遗传算法 (OGA),改善了COVID-19,子宫内膜异位症和登革热检测的分类. 这种化学测量方法增强了疾病诊断和生物标志物的发现.

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科学领域:

  • 化学测量 化学测量 化学测量
  • 机器学习 机器学习
  • 临床诊断 临床诊断 临床诊断

背景情况:

  • 一类分类器 (OCC) 模型对于目标类的建模和控制至关重要.
  • 现有的OCC模型,如DD-SIMCA和OCPLS,需要强大的变量选择以获得最佳性能.

研究的目的:

  • 整合一个新的一类遗传算法 (OGA) 与DD-SIMCA和OCPLS模型.
  • 评估临床应用中的增强分类性能,包括COVID-19,子宫内膜异位症和登革热.

主要方法:

  • 实施DD-SIMCA与alpha=0.05和OCPLS与部分强大的M回归 (PRM).
  • 这些OCC模型与变量选择的一类遗传算法 (OGA) 的关联.
  • 在临床数据集上应用和比较OGA-DD-SIMCA和OGA-PRM-OCPLS模型.

主要成果:

  • 在所有三个临床应用中,OGA显著提高了分类性能.
  • OGA-PRM-OCPLS实现了对COVID-19和子宫内膜异位症的100%敏感性.
  • OGA-DD-SIMCA在登革热分类方面表现优异,灵敏度为100%.

结论:

  • 该OGA增强了OCC模型用于疾病分类和生物标志物识别的实用性.
  • 这种化学测量方法为开发快速,低成本和最少侵入性的诊断查方法提供了潜力.