COVID-19 Mortality Risk Prediction Model Using Machine Learning

Authors

  • Alba Maribel Sánchez-Gálvez Benemérita Universidad Autónoma de Puebla
  • Sully Sánchez-Gálvez Benemérita Universidad Autónoma de Puebla
  • Ricardo Álvarez-González Benemérita Universidad Autónoma de Puebla
  • Frida Rojas-Alarcon Benemérita Universidad Autónoma de Puebla

DOI:

https://doi.org/10.13053/cys-27-4-4283

Keywords:

Machine learning algorithms, ablation study, COVID-19

Abstract

The COVID-19 outbreak commenced in Wuhan, China, in December 2019 and swiftly disseminated worldwide. On March 11, 2020, the World Health Organization (WHO) formally designated the COVID-19 outbreak as a global pandemic [1]. This highly contagious disease, which has also started to spread among young people, necessitates the implementation of policies to avert the collapse of hospitals due to shortages in beds, mechanical ventilators, and intensive care units. Using the record from March 2020 to May 31 2021, (before the arrival of vaccines in Mexico) of the website of the General Directorate of Epidemiology of the Ministry of Health of the Government of the Mexican Republic, with more than seven million people associated with COVID-19, a model is built with twelve characteristics that predicts the risk of death associated with COVID-19, applying Supervised Learning Algorithms such as Linear Regression, Naive Bayes, Decision Trees and Random Forests. The results of the machine learning algorithms show a performance of 87%. Subsequently, the model was tested again with 402,116 COVID-19-associated patients from the month of June, achieving an accuracy of 91%, surpassing the model proposed in [3]. When performing data cleaning, we observed that the twelve variables are not correlated, unlike what the authors showed in [4], where data cleaning was not carried out. This analysis can aid in designing strategies and policies to combat its spread and prevent mortality, as well as assist hospitals in prioritizing the care of higher-risk patients. 

Author Biographies

Alba Maribel Sánchez-Gálvez, Benemérita Universidad Autónoma de Puebla

Facultad de Ciencias de la Computación

Sully Sánchez-Gálvez, Benemérita Universidad Autónoma de Puebla

Facultad de Ciencias de la Computación

Ricardo Álvarez-González, Benemérita Universidad Autónoma de Puebla

Facultad de Ciencias de la Electrónica

Frida Rojas-Alarcon, Benemérita Universidad Autónoma de Puebla

Facultad de Ciencias de la Computación

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Published

2023-12-17

Issue

Vol. 27 No. 4 (2023): 27(4) 2023

Section

Articles

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