一个优化的机器学习模型,用于预测维护透析人群中COVID-19感染住院治疗的情况
Shuangshan Bu1, HuanHuan Zheng1, Shanshan Chen1
1Department of Nephrology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, 322100, China.
Computers in biology and medicine
|September 6, 2023
概括
这项研究开发了一种机器学习模型,用于预测透析患者的COVID-19感染风险,达到95.61%的准确性. 像蛋白质和血红蛋白这样的关键指标对于临床诊断至关重要.
科学领域:
- 科和传染病流行病学
- 计算生物学和机器学习
背景情况:
- 透析患者面临高的COVID-19感染率和死亡风险.
- 关于中国透析患者中COVID-19患病率和死亡率的数据有限.
- 机器学习模型为疾病预测提供了潜力,但需要功能优化和可解释性.
研究的目的:
- 开发和验证一种新的机器学习模型,用于预测透析患者的COVID-19感染风险.
- 确定用于早期诊断和风险评估的关键临床指标.
- 在预测建模中应对数据冗余和模型可解释性的挑战.
主要方法:
- 提出了一种包装特征选择分类模型,使用了增强的JAYA优化算法,以及分散的食和贪的征税突变策略.
- 为预测准确性和模型解释性优化了功能集.
- 采用模糊的K-最近邻居进行分类,并对基准函数和尿血病数据集的模型进行了验证.
主要成果:
- 在透析患者中实现了95.61%的高预测准确度,用于COVID-19感染风险.
- 确定了益白蛋白,C反应蛋白 (CRP),直接胆红素,血红蛋白,白蛋白和作为重要的预测因素.
- 证明了模型在优化特征选择和分类方面的有效性.
结论:
- 拟议的机器学习方法为预测透析患者COVID-19风险提供了一个有希望的工具.
- 特定的生物标志物 (益白蛋白,CRP,直接胆红素,血红蛋白,白蛋白,) 对于临床风险评估至关重要.
- 该方法提高了预测准确度,并提供了对重要诊断因素的洞察力.
关键词:
在 COVID-19 疫情中,分类 分类 分类 分类.一个透析患者的透析患者.毛的K-最近的邻居哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈机器学习 机器学习更多相关视频
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