多模态深度学习分类器的基于偏差支持的模糊组合用于乳腺癌预后预测
Nikhilanand Arya1, Sriparna Saha2
1Department of Computer Science & Engineering, Indian Institute of Technology Patna, Bihar, 801106, India. nikhilanand_1921cs24@iitp.ac.in.
Scientific reports
|December 3, 2023
概括
这项研究引入了一种新的组合方法,以提高乳腺癌存活率预测的准确性. 新方法考虑了分类器的信心和不确定性,优于现有方法.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 乳腺癌是全球女性死亡的主要原因,需要准确的生存预测才能进行有效的治疗.
- 分析复杂的,多来源数据来预测癌症,由于癌症病例的发病率不断增加,这带来了重大挑战.
- 早期检测和准确的预后对于改善患者的治疗结果和减少乳腺癌的负担至关重要.
研究的目的:
- 开发和评估一种新的集体机器学习方法,以提高乳腺癌存活率预测的准确性.
- 引入一个独特的组合机制,将基准分类器的偏差和支持分数纳入其中,解决传统组合方法的局限性.
- 为了利用模糊的积分来汇总分类器分数,在预测过程中明确考虑分类器的信心和不确定性.
主要方法:
- 开发了一种新的整体方法,集成模糊的积分来结合从基本分类器的支持和偏差得分.
- 整体方法独特地解释了预测和实际类之间的接近 (偏差) 和稀疏 (支).
- 拟议的模型使用来自METABRIC试验参与者的多模式乳腺癌数据集进行了验证.
主要成果:
- 新型组合方法实现了高性能指标,包括82.88%的精度,58.64%的灵敏度,62.94%的F1分数和74.75%的平衡精度.
- 与个人基准分类器相比,拟议的方法显示出更高的性能.
- 结果表明,新组合在乳腺癌生存率预测方面显著优于现有的组合方法.
结论:
- 开发的基于模糊整体的组合方法有效地提高了乳腺癌存活率预测的准确性.
- 考虑偏差和支持得分,以及分类器的信心,比传统的合奏技术有了显著的进步.
- 这种方法为乳腺癌更准确的预后提供了有希望的工具,有助于临床决策和患者管理.
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