区块链和可解释的AI集成系统用于检测多囊性卵巢综合征 (PCOS)
Gowthami Jaganathan1, Shanthi Natesan1
1Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamilnadu, India.
PeerJ. Computer science
|March 10, 2025
概括
这项研究引入了一种新的区块链和机器学习 (ML) 系统,用于安全预测多囊性卵巢综合征 (PCOS). 基于可解释的人工智能的区块链系统 (EAIBS-PCOS) 实现了高准确度的可靠PCOS诊断.
科学领域:
- 数字健康数字健康
- 区块链技术 区块链技术
- 医疗保健中的机器学习
背景情况:
- 数字化需要安全的医疗数据管理.
- 整合区块链和机器学习 (ML) 对于安全的健康数据分析至关重要.
- 现有的方法缺乏强大的数据安全性和模型可解释性,用于诸如多囊性卵巢综合征 (PCOS) 等疾病.
研究的目的:
- 开发一种用于安全存储患者医疗数据和PCOS预测的新方法.
- 为了利用Hyperledger Fabric进行不可变的私人数据存储.
- 整合可解释的人工智能 (XAI) 以实现医疗保健中的透明决策.
主要方法:
- 利用Hyperledger Fabric区块链进行安全和不变的数据存储.
- 集成机器学习 (ML) 算法用于PCOS预测.
- 采用可解释的人工智能 (XAI) 技术来确保模型的可解释性.
- 通过使用Caliper工具评估Hyperledger Fabric的性能.
主要成果:
- 拟议的基于AI的区块链系统用于检测多囊性卵巢综合征 (EAIBS-PCOS) 显示出高性能.
- 实现了98%的准确性,100%的精度,98.04%的回忆力和99.01%的F1得分.
- 验证了系统在提供可靠和可理解的PCOS预测方面的有效性.
结论:
- 区块链,ML和XAI的独特集成解决了关键的医疗保健数据安全性和可解释性问题.
- 该EAIBS-PCOS系统为安全和准确的PCOS诊断提供了一个强大的解决方案.
- 这种方法对确保医疗保健应用程序和PCOS预测文献的安全作出了重大贡献.
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