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一种基于聚合系数排名的新型特征选择策略,用于增强使用机器学习进行乳腺癌分类的诊断

E Sreehari¹, L D Dhinesh Babu²

¹School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.

Scientific reports

|February 5, 2025

概括

本研究介绍了基于聚合系数排名的特征选择 (ACRFS) 以进行有效的乳腺癌分析. ACRFS 改进了特征选择,使得乳腺癌诊断更准确,更有效,使用更少的计算资源.

科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
数据科学数据科学数据科学

背景情况:

有效的乳腺癌 (BC) 分析对于预后,复发控制和治疗计划至关重要.
当前的特征选择方法经常面临诸如复杂性和不准确性等局限性.
需要强大的特征选择,以改善BC数据分析和患者的治疗结果.

研究的目的:

提出一种新的特征选择方法,即基于聚合系数排名的特征选择 (ACRFS),用于增强乳腺癌分析.
在准确性和复杂性方面解决单个特征选择策略的局限性.
改善乳腺癌数据集的排名和属性子集选择.

主要方法:

开发了基于聚合系数排名的特征选择 (ACRFS) 方法,使用三特征行为标准.
采用计算技术,包括千平方,相互信息,相关性和等级密度方法.
利用威斯康星州的乳腺癌数据,应用合成少数群体过量采样技术 (SMOTE),并测试了各种分类模型 (决策树,SVM,KNN,随机森林,SGD,GNB).

主要成果:

在乳腺癌分类方面,ACRFS方法表现出卓越的性能.
通过减少所选特征的数量,实现了准确的分类.
与现有方法相比,表现出最小的时间复杂性.

结论:

拟议的ACRFS方法为乳腺癌特征选择提供了一种有效和高效的方法.
ACRFS提高了分类准确性,并减少了计算负担.
这种方法有望改善乳腺癌诊断和患者管理.

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