基于韩国成年人身体组成预测高血压的机器学习方法
Jeong-Woo Seo1, Sanghun Lee2, Mi Hong Yim1
1Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34504, Republic of Korea.
Bioengineering (Basel, Switzerland)
|September 27, 2024
概括
机器学习模型预测韩国成年人的高血压. 年龄,骨肌肉质量和体脂质量是男性的关键预测因素,而年龄和体脂质量对女性来说是显著的.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 高血压是成年人严重的健康问题.
- 身体组成,特别是肌肉和脂肪,与高血压风险有关.
- 预测模型可以帮助早期检测高血压.
研究的目的:
- 为了确定韩国成年人中高血压的显著体质成分预测因素.
- 在不同的机器学习技术中比较预测准确度.
- 确定高血压的性别特异性预测因子.
主要方法:
- 利用六种机器学习技术来分类高血压.
- 包括2906名韩国成年人的年龄,BMI,体脂质量,肌肉质量和体液数据.
- 开发了高血压分类的预测模型.
主要成果:
- 弹性网模型实现了最高的分类准确性.
- 对于男性来说,年龄,骨肌肉质量 (SMM) 和体脂质量 (BFM) 是最具预测性的.
- 对于女性来说,年龄和BFM是显著的预测因素;SMM和软瘦质量没有显示差异.
结论:
- 高血压与男性的体脂质量和骨肌肉质量有关.
- 与骨肌肉质量相比,身体脂肪质量对女性的高血压有更大的影响.
- 机器学习有效地识别了用于高血压预测的身体组成关键指标.
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