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生物化学和血液学因素与COVID-19之间的关联使用数据挖掘方法.

Amin Mansoori1,2,3, Nafiseh Hosseini1,4, Hamideh Ghazizadeh1,5

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概括

使用关键的人口,生化和血液学因素,可以预测COVID-19感染. 肌酸化酶 (CPK),体重指数 (BMI) 和年龄是患者分类所确定的关键指标之一.

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科学领域:

  • 医疗信息学 医疗信息学
  • 流行病学 流行病学
  • 生物化学 生物化学

背景情况:

  • 冠状病毒疾病 (COVID-19) 是一种迅速传播的传染病,具有重大公共卫生影响.
  • 识别COVID-19感染的关键因素有助于医疗保健专业人员在患者管理中.
  • 预测模型可以帮助区分受感染者和未受感染者.

研究的目的:

  • 确定与COVID-19感染相关的关键的人口,生化和血液学特征.
  • 评估机器学习模型在预测COVID-19状态方面的有效性.
  • 确定关键预测因素来分类COVID-19和没有COVID-19的患者.

主要方法:

  • 使用了13,170名年龄在35-65岁之间的参与者的数据集.
  • 使用的决策树 (DT),物流回归 (LR) 和引导森林 (BF) 算法.
  • 开发了三种分析生化特征,血液学特征和两者的组合模型.

主要成果:

  • 模型I (生物化学):确定了肌酶 (CPK),血尿素 (BUN),禁食血糖 (FBG),总胆红素,体重指数 (BMI),性别和年龄作为预测因素.
  • 模型II (血液学):确定BMI,性别,平均血小板体积 (MPV) 和年龄作为预测因素.
  • 模型III (组合):确定了CPK,BMI,MPV,BUN,FBG,性别,肌素 (Cr),年龄和总胆红素作为重要的预测因素.

结论:

  • 引导森林 (BF),决策树 (DT) 和后勤回归 (LR) 模型有效预测和分类COVID-19状态.
  • COVID-19的主要预测因素包括CPK,BUN,BMI,MPV,FBG,性别,Cr和年龄.
  • 这些已识别的因素与COVID-19感染有着强烈的关联,有助于患者的分类.