Skip to main navigation menu Skip to main content Skip to site footer
×
Español (España) | English
Editorial
Home
Indexing
Original

Statistical analysis and decision trees to identify risk factors in the Mexican population due to COVID-19 pandemic

By
Itzel Paola Cervera Arguelles ,
Itzel Paola Cervera Arguelles

Universidad Autónoma de Aguascalientes, Departamento de Ciencias de la Computación. Aguascalientes, México

Search this author on:

PubMed | Google Scholar
Hermilo Sánchez Cruz ,
Hermilo Sánchez Cruz

Universidad Autónoma de Aguascalientes, Departamento de Ciencias de la Computación. Aguascalientes, México

Search this author on:

PubMed | Google Scholar

Abstract

Introduction: The COVID-19 pandemic caused by the new SARS-CoV-2 virus was a big challenge to the world and was responsible for a vast number of deaths in a brief period; one of the countries with the greatest number of deaths was México. For this reason, studying this emergency is crucial.
Objective: study and compare the available statistics for Mexico about the COVID-19 pandemic and build a machine learning model that helps to identify the risk factors of the Mexican population.
Methods: This research is structured into three sections. Firstly, a worldwide and national statistical analysis, then a decision tree-based model, and lastly, research about the results of the vaccination campaign. Different databases were used to fulfill the objectives of each section.
Results: With international information, the number of cases and deaths were studied for a group of countries; in addition, this study compared daily cases and deceases in México, Colombia, and Spain. The national data was used to obtain different statistics and a decision tree-based model. For the vaccination campaign, various statistics were gathered.
Conclusions: Even though international statistics did not help determine if comorbidities had a significant effect on deceases, national statistics indicate that they were a risk factor for passing away due to COVID-19. Similarly, the decision tree model indicated that hospitalization was a common characteristic among deceased people. For the vaccination campaign, the lack of data was a problem in identifying the role this event had in the development of the pandemic; nevertheless, the international surveillance systems received an exceptional number of reports about adverse events; for this reason, each person should decide if they need a vaccine.

How to Cite

1.
Cervera Arguelles IP, Sánchez Cruz H. Statistical analysis and decision trees to identify risk factors in the Mexican population due to COVID-19 pandemic. Salud, Ciencia y Tecnología [Internet]. 2024 May 7 [cited 2024 May 28];4:790. Available from: https://revista.saludcyt.ar/ojs/index.php/sct/article/view/790

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.