基于特征选择的高效堆叠模型用于基于胸部皮电活动的压力检测
Ahmad Almadhor1, Gabriel Avelino Sampedro2,3, Mideth Abisado4
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新的机器学习堆叠模型,用于使用可穿戴传感器早期检测压力. 该模型实现了高精度,为改进的人工智能驱动的医疗保健解决方案铺平了道路.
科学领域:
- 生理监测和可穿戴技术.
- 机器学习在医疗保健中的应用.
- 压力检测和管理.
背景情况:
- 可穿戴设备为压力监测提供连续的生理数据.
- 早期检测压力可以减轻慢性压力影响.
- 机器学习 (ML) 和人工智能 (AI) 在医疗保健中越来越多地使用,但需要更多的数据来实现更广泛的应用.
研究的目的:
- 开发和评估一个堆叠模型,用于使用胸部特征检测压力.
- 利用可穿戴式压力和影响检测 (WESAD) 数据集用于压力识别.
- 通过改进数据处理和建模,增强AI在医学诊断中的应用.
主要方法:
- 数据预处理和WESAD数据集的可视化.
- 使用Z-score,SelectKBest和合成少数人过量采样技术 (SMOTE) 进行特征提取和选择.
- 开发一个堆叠模型,集成多个机器学习算法来进行应力分类.
主要成果:
- 拟议的堆叠模型在应力检测中实现了0.99%的准确性.
- 与传统方法相比,该模型显示出更高的性能.
- 该方法在使用生理数据对压力水平进行分类方面被证明是有效的.
结论:
- 开发的堆叠模型显示了精确的应力检测的巨大潜力.
- 这种方法推进了在个性化医疗保健中使用可穿戴传感器数据和ML的使用.
- 使用更大的数据集进行进一步的研究可以促进在医疗监测中更广泛地采用人工智能.
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