A set of procedures that can realize covid19 virus detection based on blood.

Overview

COVID19_detection

背景

​ 当前,世界正遭受全球COVID19大流行的困扰。数十亿人受到影响,数百万的人员伤亡已经发生。因此,鉴定受SARS-CoV-2病毒感染或已经受其污染的个人至关重要。 这种识别有助于公共卫生组织和政府制定行动计划,以减少这种大流行的影响。从这种意义上讲,Hilab是一家远程实验室公司,它执行数十种类型的血液检查,包括针对COVID19的血清学检查,该公司已经在巴西进行了数百万次检查。为了改善对这种病毒的检测,可以使用机器学习方法来帮助实验室专家进行决策。 因此,本项目将致力于解决构建用于检测COVID19的具有高置信度和准确性的机器学习模型的难题。

方法

  • 决策树(Decision tree)
  • 随机森林(Random forest)
  • 支持向量机(SVN)
  • 主成分分析(PCA)

数据集

数据集地址:https://drive.google.com/drive/folders/1FfIx5WmEc_C7d3Ai7ONIQE4s-o2xQZz5?usp=sharing

项目结构

/
-dataset/		#数据集存放目录
--test/			#测试集目录
---test.csv		#测试集文件
--train/  		#训练集目录
---train_1.csv	#训练集文件1(此文件与测试集相同,默认不使用)
---train_2.csv	#训练集文件2
.......
---train_7.csv	#训练集文件7

-data_preprocess.py	#数据集提取与预处理
-pca.py				#pca降维的相关实验
-decision_tree.py	#决策树
-random_forest.py	#随机森林
-SVM.py				#SVM
-README.md			#说明文件
Owner
Nuyoah-xlh
Nuyoah-xlh
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