综合分类器和基于过器的特征选择的结构化组合,以改善乳腺癌诊断
Dengru Zheng1, Ping Tang2, Danping Lu2
1Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China. 15030440116@xs.hnit.edu.cn.
Journal of cancer research and clinical oncology
|August 11, 2023
概括
这项研究引入了一种智能机器学习方法,用于早期发现乳腺癌. 该方法通过优化特征选择和整体分类器提高了诊断准确性,改进了现有的算法.
科学领域:
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
- 生物医学工程 生物医学工程
背景情况:
- 机器学习和计算机化诊断系统正在成为智能医疗助理.
- 早期发现乳腺癌对于有效治疗至关重要.
- 数据挖掘技术,如特征选择和分类,提供高速,低成本,精确的乳腺癌诊断.
研究的目的:
- 提出一种新的智能方法来诊断乳腺癌.
- 集成高级功能选择和优化的集体分类器.
- 提高乳腺癌早期检测系统的准确性和效率.
主要方法:
- 一种基于进化搜索的集成过器特征选择方法,结合了自适应值者信息获取和进化引力搜索.
- 一个优化的集体分类器,利用多层感知神经网络.
- 对威斯康星州乳腺癌数据集的评估 (WBCD,WDBC,WPBC).
主要成果:
- 与最先进的算法相比,拟议的方法取得了更高的性能.
- 使用仅56.7%的功能,实现了99.42%的平均准确性.
- 在乳腺癌分类中表现出高精度,灵敏度和特异性.
结论:
- 由机器学习驱动的智能医疗助理系统代表了早期发现乳腺癌的重大进步.
- 这些系统可以帮助医疗专业人员更有效地诊断乳腺癌.
- 拟议的方法有助于开发更复杂的癌症治疗诊断工具.
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