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Updated: May 21, 2025

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通过使用二进制Al Biruni地球半径算法的特征选择改进了癌症检测.

El-Sayed M El-Kenawy1, Nima Khodadadi2, Marwa M Eid3

  • 1School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain.

Scientific reports
|March 20, 2025
PubMed
概括

一个新的二进制高级Al-Biruni地球半径 (bABER) 算法有效地选择了癌症检测的关键特征. 这种方法提高了机器学习模型的准确性,从而改善了医疗预测和更快的诊断.

关键词:
阿尔-比鲁尼地球半径优化算法癌症治疗 治疗 癌症治疗功能选择 功能选择医疗数据集是一个医疗数据集.

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科学领域:

  • 计算生物学 计算生物学
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 医疗技术产生了大量复杂的癌症数据,用于诊断和治疗.
  • 大数据集中的冗余或无关的特征可能会降低机器学习模型的准确性.
  • 超启发式算法用于特征选择,但可扩展性和效率仍然是挑战.

研究的目的:

  • 提出高级Al-Biruni地球半径 (bABER) 算法的二进制版本,用于癌症检测中的智能数据减少和特征识别.
  • 解决现有的元启发算法在处理大型医疗数据集方面的局限性.

主要方法:

  • 开发了一个新的二进制算法,bABER,用于特征选择.
  • 在七个不同的医疗数据集上评估了bABER.
  • 性能与八个已建立的二进制元启发算法 (bSC,bPSO,bWAO,bGWO,bMVO,bSBO,bFA,bGA) 进行了比较.
  • 为了进行严格的评估,进行了统计分析,包括ANOVA和Wilcoxon签名等级测试.

主要成果:

  • 与所有其他评估方法相比,bABER算法在统计学上表现出显著的优异性能.
  • 取得了有效的基本特征识别和不必要数据的删除.
  • 观察到机器学习模型用于癌症预测的准确性和可靠性得到了提高.

结论:

  • bABER算法是通过优化特征选择改进癌症诊断的高效工具.
  • 这种方法提高了现有的机器学习模型的性能,从而导致更精确的医学预测.
  • 该研究有助于推进数据驱动的医疗保健决策,以更快,更准确地检测癌症.