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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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通过使用区块链安全的混合遗传算法推进乳腺癌预测.

Monu Bhagat1, Ujjwal Maulik2

  • 1Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India; Department of Artificial Intelligence, Indian Institute of Technology Kharagpur, India.

Computers in biology and medicine
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概括

这项研究将进化算法和区块链与机器学习相结合,用于乳腺癌预测. 综合方法实现了高准确性,提高了早期诊断,并保护了患者数据的隐私.

关键词:
区块链 区块链 区块链 区块链乳腺癌是什么? 乳腺癌是什么功能选择 功能选择遗传算法 (GA) 是一种基因算法.机器学习是机器学习.预测 预后 预测 预测

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算机科学 计算机科学
  • 生物技术是生物技术.

背景情况:

  • 在机器学习中,特征选择 (FS) 对于提高模型性能和精度至关重要.
  • 通过机器学习 (ML) 可以提高乳腺癌 (BC) 预测,同时通过区块链技术确保数据隐私和完整性.

研究的目的:

  • 探索使用进化算法 (GA) 来预测乳腺癌的特征选择.
  • 开发和评估一个安全的系统,将区块链与ML集成为乳腺癌诊断.
  • 为了比较各种ML算法和分类器用于乳腺癌识别的性能.

主要方法:

  • 使用了威斯康星州诊断乳腺癌数据集.
  • 实现并比较多个ML算法,包括XGBoost,AdaBoost,后勤回归,线性SVM,随机森林,KNN,梯度提升,辐射SVM和决策树.
  • 将区块链方法与智能合约集成为安全的数据处理和预测.

主要成果:

  • 所有测试的ML算法在区分良性瘤和恶性瘤方面都取得了超过96.31%的准确性.
  • 基因算法 (GA) 和线性SVM的组合产生了最高的准确率99.47%.
  • 总体而言,XGBoost表现出卓越的性能,超过了具有或没有GA的其他算法.

结论:

  • 监督机器学习技术,特别是与进化算法和区块链相结合时,显示出早期癌症诊断和预后的重大前景.
  • 开发的系统提供了一种安全且无透的解决方案,可以防止数据泄露和改医疗预测过程.
  • 将区块链与ML集成,提高了乳腺癌预测模型的可靠性和安全性.