通过组合模型和不平衡的数据集处理,基于光电脉冲扫描的非侵入性血压监测
Qianyu Liu1, Chaojie Yang2,3, Sen Yang4,5
1School of Information Science and Engineering, NingboTech University, Ningbo, China.
Physical and engineering sciences in medicine
|July 30, 2024
概括
这项研究引入了使用光梯度增强机 (LightGBM) 进行准确的非侵入性血压预测的合奏模型. 该模型有效地使用合成少数人过量采样技术 (SMOTE) 来解决数据不平衡问题,提高了分类性能.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 心血管监测 心血管监测
背景情况:
- 光电显微镜 (PPG) 是一种用于血压 (BP) 监测的非侵入性方法.
- 单个机器学习模型通常在BP预测中缺乏准确性.
- 数据失衡是医疗数据集中常见的挑战,可能会影响分类结果.
研究的目的:
- 开发一个准确的组合模型,用PPG信号来预测心压和腹压血压.
- 评估合成少数人过量采样技术 (SMOTE) 在处理不平衡的医疗数据集进行分类任务时的有效性.
主要方法:
- 为了BP预测,开发了一种用光渐变增强机 (LightGBM) 作为基础估计器的整体模型.
- 为了解决数据不平衡,采用了三个最近邻居的合成少数人过量采样技术 (SMOTE).
- 这项研究包括115名女性和104名男性.
主要成果:
- 整体模型的平均绝对误差为5.63 mmHg对于腹血压和9.36 mmHg对于心血压,符合英国高血压协会的标准.
- 对于分类任务,SMOTE应用程序的F1得分为81.6%,曲线下的面积 (AUC) 为0.895.
- 拟议的SMOTE方法在不平衡数据集的其他技术相比显示出更高的性能.
结论:
- 开发的整体模型显示了准确的非侵入性血压预测的巨大潜力.
- SMOTE是一种有效的技术,用于管理医学研究中适度不平衡的数据集,提高分类性能.
- 这种方法为改善BP监测和解决临床应用中的数据挑战提供了有希望的方向.
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