Research on Medical Decision Machine Learning Method for Predicting COVID-19 Infection Risk

GUO Wei; CHEN Guang-xin; YU Miao; YU Guang-hao; GUO Jin-xing

doi:10.3969/j.issn.2096-6091.2023.13.003

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Research on Medical Decision Machine Learning Method for Predicting COVID-19 Infection Risk

RESEARCH PAPER | 更新时间：2025-03-26

- Research on Medical Decision Machine Learning Method for Predicting COVID-19 Infection Risk
- New Generation of Information Technology Vol. 6, Issue 13, Pages: 12-17(2023)
- 作者机构：
  
  1.牡丹江医学院生命科学学院，黑龙江牡丹江 157011
  2.牡丹江医学院医学影像学院，黑龙江牡丹江 157011
  3.牡丹江医学院附属红旗医院，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.13.003
  CLC： TP391.41;TP18
- Accepted：2023-06-26，
  
  Published：15 July 2023
- 稿件说明：
移动端阅览
国威, 陈广新, 于淼, 等. 预测COVID-19感染风险的医疗决策机器学习方法研究[J]. 新一代信息技术, 2023, 6(13): 12-17

GUO Wei, CHEN Guang-xin, YU Miao, et al. Research on Medical Decision Machine Learning Method for Predicting COVID-19 Infection Risk[J]. New Generation of Information Technology, 2023, 6(13): 12-17
国威, 陈广新, 于淼, 等. 预测COVID-19感染风险的医疗决策机器学习方法研究[J]. 新一代信息技术, 2023, 6(13): 12-17 DOI： 10.3969/j.issn.2096-6091.2023.13.003.

GUO Wei, CHEN Guang-xin, YU Miao, et al. Research on Medical Decision Machine Learning Method for Predicting COVID-19 Infection Risk[J]. New Generation of Information Technology, 2023, 6(13): 12-17 DOI： 10.3969/j.issn.2096-6091.2023.13.003.

摘要

开发一种机器学习模型，用于预测个体是否处于受COVID-19（COrona VIrus Disease 2019）影响的危险之中，并辅助医疗决策，包括就医或选择居家隔离。基于GradientBoost、XGBoost、随机森林3种集成学习算法以及决策树、逻辑回归、支持向量机、KNN算法4种非集成学习算法，构建COVID-19风险预测模型并验证模型效能，识别COVID-19风险因素。集成学习与非集成学习模型ROC曲线下面积大致都在0.94左右。同时识别出年龄、是否是住院患者、是否携带病毒、是否怀孕、是否患有肺炎、是否插氧等重要风险因素。结果表明，在大样本量下集成学习不一定会优于非集成学习方法。

Abstract

To develop a machine learning model to predict whether an individual is at risk from COVID-19 (Corona Virus Disease 2019) and to aid medical decisions

including seeking medical attention or choosing home isolation. Based on three integrated learning algorithms

GradientBoost

XGBoost and Stochastic Forest

as well as four non-integrated learning algorithms including decision tree

logistic regression

support vector machine and KNN (K-Nearest Neighbor) algorithm were used to construct a COVID-19 risk prediction model. We validated the model efficiency

and identified the COVID-19 risk factors. The area under ROC (Receiver Operating Characteristic) curve of both integrated learning and non-integrated learning models was approximately 0.94. Important risk factors

such as age

hospitalization

infection

pregnancy

pneumonia

and oxygen insertion

were also identified. Integrated learning is not necessarily superior to non-integrated learning in large sample size.

关键词

Keywords

references

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Related Institution

Hongqi Hospital Affiliated to Mudanjiang Medical University

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