IoMT与机器学习相遇:从边缘到云 慢性疾病诊断系统
Natasha Nigar1, Abdul Jaleel1, Shahid Islam1
1Department of Computer Science (RCET), University of Engineering and Technology, Lahore, Pakistan.
Journal of healthcare engineering
|June 12, 2023
概括
这项研究引入了一种新的物联网 (IoT) 和机器学习 (ML) 方法,用于早期检测六种慢性疾病. 混合模型增强了患者监测和及时诊断,改善了医疗保健结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 及时诊断慢性疾病对于预防死亡至关重要.
- 传统的医疗保健系统需要有效的实时患者监测.
- 物联网 (IoT) 提供了用于医疗状况跟踪的自主传感.
研究的目的:
- 提出一种使用物联网和机器学习 (ML) 的新型混合方法,用于早期检测和监测慢性疾病.
- 评估多个ML模型的准确性,精度,回忆,F1得分和AUC的性能.
- 通过使用基准和现实世界的数据集,在基于云的环境中验证拟议的方法.
主要方法:
- 开发一种混合物联网和机器学习模型.
- 实现多个机器学习分类器用于比较分析.
- 在云环境中使用基准和真实世界的数据集进行验证.
- 使用ANOVA测试进行统计分析,以比较分类器性能.
主要成果:
- 拟议的混合物联网-ML方法在早期发现和监测慢性疾病方面表现出有效性.
- 对比分析显示,各种ML模型的性能指标 (准确性,精度,回忆,F1得分,AUC) 有显著差异.
- 统计验证证实了分类器准确性的显著差异.
结论:
- 开发的基于物联网和机器学习的混合方法有助于早期诊断和监测慢性疾病.
- 这项技术可以显著帮助医疗保健部门和临床医生改善患者的治疗结果.
- 这些发现支持将物联网和机器学习整合到高级慢性疾病管理中.
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