在医疗保健行业使用适应性FL进行多学科癌症疾病分类 5.0
Tahir Abbas1, Areej Fatima2, Tariq Shahzad3
1School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan.
Scientific reports
|August 11, 2024
概括
联合学习通过使模型训练能够在不集中敏感患者数据的情况下增强医疗保健AI. 这种适应性方法在工业5.0环境中提高了多学科癌症疾病预测的准确性.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 工业5.0需要人工智能和数据驱动的应用程序,并提供强大的数据保护.
- 联合学习 (FL) 允许在没有直接访问分布式培训数据的情况下进行模型改进.
- 医疗保健系统需要专业的数据隐私多主应用程序.
研究的目的:
- 为智能医疗保健系统引入自我适应的联合学习框架.
- 为了解决工业5.0医疗保健中的数据保护和隐私问题.
- 为了提高多学科癌症疾病预测的准确性.
主要方法:
- 开发了一个自我适应的框架,用于医疗保健中的联合机器学习.
- 模型在每个医院本地接受培训,并在中央优化.
- 数据集被分割为多任务优化,设备选择最佳子集.
- 集成联合学习与深度机器学习模型用于癌症预测.
主要成果:
- 在不同的医院和设备数量中演示了算法融合.
- 通过拟议的自适应联合机器学习方法来实现90.0%的准确性.
- 传统的联合学习实现了87.30%的准确性.
- 这两种方法都超过了先前在癌症疾病预测方面的先进技术,用于智能医疗行业5.0.0.
结论:
- 适应式联合学习框架有效地提高了癌症疾病预测的准确性.
- 该方法尊重数据隐私,同时利用分布式医疗数据.
- 这种方法对工业5.0中的智能医疗保健应用具有前景.
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