混合机器学习方法对乳腺癌和复发预测的性能评估
Abhilash Pati1, Amrutanshu Panigrahi1, Manoranjan Parhi2
1Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India.
PloS one
|August 1, 2024
概括
这项研究引入了递归特征消除 (RFE) 和灰狼优化器 (GWO) 来改进用于乳腺癌诊断和复发预测的机器学习模型,实现高精度. 混合方法提高了乳腺癌的早期检测和分类,有助于改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 计算机科学 计算机科学
- 生物信息学是一种生物信息学.
背景情况:
- 乳腺癌复发和转移是导致死亡的主要原因.
- 准确的诊断和早期发现复发对于患者的生存至关重要.
- 现有的机器学习模型面临着大数据集和特征选择的挑战.
研究的目的:
- 评估和比较用于乳腺癌诊断和复发预测的机器学习技术.
- 解决特征选择的局限性,以提高预测准确度.
- 引入一种混合方法,使用递归特征消除 (RFE) 和灰狼优化器 (GWO) 进行增强的乳腺癌分类.
主要方法:
- 利用了威斯康星州诊断乳腺癌 (WDBC) 和威斯康星州诊断乳腺癌 (WPBC) 数据集.
- 应用数据预处理技术,包括归算和缩放.
- 使用递归特征消除 (RFE) 来进行特征选择,使用灰狼优化器 (GWO) 来进行特征优化.
- 集成了七个机器学习分类器用于二进制分类任务.
主要成果:
- 在WDBC和WPBC数据集上实现了高性能指标.
- 报告的准确率为98.25% (WDBC) 和93.27% (WPBC).
- 通过混合RFE-GWO方法证明了提高精度,灵敏度,特异性,F1得分和AUC值.
结论:
- 混合RFE-GWO方法显著提高了乳腺癌诊断和复发预测的准确性.
- 有效的特征选择和优化对于提高机器学习模型在瘤学中的性能至关重要.
- 这种方法为乳腺癌的早期检测和分类提供了一个有希望的工具,有可能改善患者的治疗结果.
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