通过使用区块链安全的混合遗传算法推进乳腺癌预测.
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
|July 4, 2025
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.
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.
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