通过医疗物联网设备使用多式联络特征的预测性健康行为建模
Moshe Dayan Sirapangi1, S Gopikrishnan1
1School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India.
Heliyon
|August 15, 2024
概括
这项研究引入了一种使用物联网 (IoT) 设备和深度学习进行健康预测的新行为建模方法. 该模型增强了健康结果的预测,并建议改变生活方式以获得更健康的生活.
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 设备可以监控人类的行为模式,如睡眠,活动和心率变化 (HRV).
- 现有的与健康状况相关的行为模式模型往往缺乏准确性,灵活性和可扩展性的实时应用.
研究的目的:
- 提出一种新的行为建模方法,用于使用医疗物联网设备的多式联络功能进行健康预测.
- 提高健康预测和生活方式建议的准确性和实时适用性.
主要方法:
- 从医疗物联网设备收集了大规模的传感器数据,并将其与现有的医疗条件相关联.
- 使用光谱分析,评估,缩放估计和基于窗口的分析提取了多域特征集.
- 使用火优化器 (FFO) 进行特征选择,训练一次性神经网络 (RNN) 进行疾病预测,并使用Apriori和Fuzzy C Means (FCM) 进行推引擎.
主要成果:
- 提高了16.4%的行为预测精度,8.3%的预测精度和9.5%的AUC.
- 与现有方法相比,纠正行为建议的准确性提高了3.9%.
结论:
- 拟议的深度转移学习模型有效地将多式联网物联网数据关联起来,以准确地预测健康状况.
- 该系统为健康的生活方式提供实时,个性化的建议,比传统方法显著改善.
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