对呼吸系统疾病分类的元启发性特征选择算法的比较研究
Damla Gürkan Kuntalp1, Nermin Özcan2, Okan Düzyel3
1Department of Electrical and Electronics Engineering, Dokuz Eylül University, İzmir 35160, Türkiye.
Diagnostics (Basel, Switzerland)
|October 16, 2024
概括
听优化方法有效地减少数据的维度,并提高自动呼吸道疾病分类的准确性. 这种方法提高了早期诊断和患者的结果.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 呼吸系统疾病的早期诊断和治疗对于患者的健康和降低医疗保健成本至关重要.
- 自动呼吸道疾病检测系统正在引起人们的兴趣,机器和深度学习方法占据了重要地位.
- 有效的特征选择对于机器学习在分类呼吸道疾病方面的成功至关重要.
研究的目的:
- 在呼吸道疾病分类中对特征选择进行六种元启发式优化方法进行比较分析.
- 在这些元启发式方法中评估八个不同的转移函数的影响.
- 在二元和多类呼吸道疾病分类场景中评估性能.
主要方法:
- 使用了六种不同的元启发式优化算法.
- 整合了八个不同的传输函数与元启发算法.
- 将这些方法应用于二元和多类呼吸道疾病分类任务.
- 专注于特征选择以提高分类器性能.
主要成果:
- 当与适当的转移函数配对时,metaheuristic算法证明了显著的数据维度减少.
- 选择的特征子集使呼吸道疾病的分类准确度提高.
- 对比分析提供了关于不同元启发和转移函数组合的有效性的见解.
结论:
- 听优化方法是呼吸系统疾病分类中特征选择的有效工具.
- 转移函数的选择显著影响了这个领域的元启发算法的性能.
- 这项研究突出了改善自动呼吸道疾病检测系统的有希望的途径.
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