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参数优化和机器学习和深度学习算法对乳腺癌诊断的比较研究

Parul Jain¹, Shalini Aggarwal¹, Sufiyan Adam¹

¹Department of Computer Science, Atma Ram Sanatan Dharma College, University of Delhi, New Delhi, India.

|September 27, 2024

概括

机器学习模型在诊断乳腺癌方面显示出高准确度. 像XGBoost这样的超参数调整和增强方法在威斯康星州乳腺癌数据集上显著提高了早期癌症检测性能.

科学领域:

在瘤学瘤学.
计算机科学计算机科学
数据挖掘数据挖掘

背景情况:

乳腺癌是妇女死亡的主要原因之一.
手动诊断是耗时且有限的.
需要自动化早期癌症检测系统.

研究的目的:

调查用于乳腺癌诊断的机器学习模型性能.
将人工神经网络 (ANN) 方法与传统技术进行比较.
评估威斯康星州乳腺癌 (原始) 数据集上的模型.

主要方法:

应用了各种机器学习模型,包括SVM,决策树,CART,ANN和ELM ANN.
利用超参数调整来优化模型性能.
采用XGBoost,Adaboost和梯度提升等增强算法.

主要成果:

几种分类器在良性和恶性瘤中获得了高准确度,精度和F1分数.
具有超参数调整的模型表现优于没有的模型.
增强方法在区分瘤类型方面始终表现良好.

结论:

机器学习,特别是超参数调整和增强算法,对于乳腺癌诊断是有效的.
这些技术可以解决数据的复杂性和非线性.
该研究总结了使用威斯康星州数据集对乳腺癌诊断的当前研究.

关键词:

在 Adaboost 中使用.乳腺癌是什么? 乳腺癌是什么在SVM中,SVM是SVM.决策树是一个决策树.深度学习是一种深度学习.

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