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Updated: Jun 12, 2025

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使用改进的RIME算法与正交学习进行有效的膀癌诊断.

Mosa E Hosney1, Essam H Houssein2, Mohammed R Saad1

  • 1Faculty of Computers and Information, Luxor University, Luxor, Egypt.

Computers in biology and medicine
|September 25, 2024
PubMed
概括
此摘要是机器生成的。

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一种新的混合优化方法mRIME通过选择最佳特征来提高膀癌 (BC) 分类准确性. 该mRIME-SVM模型改善了诊断结果,并证明了在生物信息学中的广泛应用.

科学领域:

  • 生物信息学和计算生物学
  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用

背景情况:

  • 准确的膀癌 (BC) 诊断对于有效的治疗计划至关重要,但从不同的数据集对瘤进行分类仍然存在挑战.
  • 现有的特征选择 (FS) 和分类方法可能会与复杂的生物数据作斗争,影响诊断精度.

研究的目的:

  • 在BC诊断中引入一种新的混合优化算法mRIME,用于包装特征选择 (FS).
  • 开发一个集成模型,mRIME-SVM,结合FS的mRIME和用于增强BC分类的SVM.
  • 评估mRIME和mRIME-SVM在全球优化任务和BC数据集上的性能.

主要方法:

  • 开发了一种混合优化算法mRIME,它结合了正角学习 (OL) 和Rime优化算法 (RIME) 来进行特征选择.
  • 集成的mRIME与支持矢量机 (SVM) 创建mRIME-SVM分类模型,使用mRIME进行超参数调整.
  • 在CEC'2022测试套件上对各种元启发算法进行了mRIME评估,并在多个BC数据集上对mRIME-SVM进行了评估.

主要成果:

  • 与已建立的算法相比,mRIME算法在解决全球优化问题方面表现出卓越的性能.
  • mRIME-SVM在九个BC数据集上实现了高分类准确性,超过了现有的模型和流行的元启发算法.
关键词:
癌症的分类 癌症的分类功能选择 (FS) 功能选择超听证学优化优化方法交叉学习 (OL) 是指对角学习.在 RIME 算法中,使用 RIME 算法.

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  • 拟议的方法有效地导航复杂的搜索空间,优化特征选择而不影响分类器性能.

    • 结论:

    • mRIME算法为各种优化任务提供了具有竞争力和有效的方法,包括功能选择.
    • mRIME-SVM提供了一个强大的计算框架,以提高精度和可靠性来推进膀癌的诊断.
    • 这项研究强调了在生物信息学和人工智能驱动的医学研究中混合人工智能方法的潜力,以提高疾病分类.