Data Mining in the COVID-19: Application of Association Rules to Analyze Epidemiological Data of Patients in Mexico

This research uses data mining methodology to extract information from a large database containing 1,048,576 records of COVID-19 patients in Mexico during 2023. The Apriori algorithm is used to find the association rules and analyze the clinical and demographic variables. The target is to find out the chance of intubation by the factor interaction. After preprocessing the data, the Apriori algorithm employs a minimum support of 21.6% and a minimum confidence of 60% to meet the market basket needs. In all, 833 association rules are produced, which establish connections between variables like lab findings, intensive care unit admission, chronic obstructive pulmonary disease or asthma diagnosis, and intubation requirements. Certain rules show very dependable correlations with confidence levels of 99%. The results enable proactive medical interventions and resource management by offering crucial information for the early identification of high-risk individuals. The study demonstrates how data mining techniques may be used to retrieve important information from huge epidemiological databases. To find hidden relationships in a large database of COVID-19 patients in Mexico, this work effectively uses the Apriori algorithm, producing vital data for public health decision-making. Received: 25 December 2024 | Revised: 24 June 2025 | Accepted: 31 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The epidemiological dataset that supports the findings of this study is openly available at the Mexican Government’s DGE repository: https://datos.covid-19.conacyt.mx/. Author Contribution Statement Bogart Yail Marquez: Conceptualization, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Raul Barutch Pimienta-Gallardo: Validation, Formal analysis, Resources, Writing – review & editing, Visualization, Funding acquisition. Arturo Realyvásquez-Vargas: Methodology, Validation, Investigation, Resources, Writing – original draft, Visualization.

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Journal
Medinformatics
Published
2025-10-13
DOI
https://doi.org/10.47852/bonviewmedin52025088
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Data Mining in the COVID-19: Application of Association Rules to Analyze Epidemiological Data of Patients in Mexico

Bogart Yail Márquez, Raúl Barutch Pimienta Gallardo, Arturo Realyvásquez-Vargas
Medinformatics
Artificial Intelligence in Healthcare
article

Data Mining in the COVID-19: Application of Association Rules to Analyze Epidemiological Data of Patients in Mexico

Bogart Yail Márquez, Raúl Barutch Pimienta Gallardo, Arturo Realyvásquez-Vargas
article en

Abstract

This research uses data mining methodology to extract information from a large database containing 1,048,576 records of COVID-19 patients in Mexico during 2023. The Apriori algorithm is used to find the association rules and analyze the clinical and demographic variables. The target is to find out the chance of intubation by the factor interaction. After preprocessing the data, the Apriori algorithm employs a minimum support of 21.6% and a minimum confidence of 60% to meet the market basket needs. In all, 833 association rules are produced, which establish connections between variables like lab findings, intensive care unit admission, chronic obstructive pulmonary disease or asthma diagnosis, and intubation requirements. Certain rules show very dependable correlations with confidence levels of 99%. The results enable proactive medical interventions and resource management by offering crucial information for the early identification of high-risk individuals. The study demonstrates how data mining techniques may be used to retrieve important information from huge epidemiological databases. To find hidden relationships in a large database of COVID-19 patients in Mexico, this work effectively uses the Apriori algorithm, producing vital data for public health decision-making. Received: 25 December 2024 | Revised: 24 June 2025 | Accepted: 31 August 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The epidemiological dataset that supports the findings of this study is openly available at the Mexican Government’s DGE repository: https://datos.covid-19.conacyt.mx/. Author Contribution Statement Bogart Yail Marquez: Conceptualization, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Raul Barutch Pimienta-Gallardo: Validation, Formal analysis, Resources, Writing – review & editing, Visualization, Funding acquisition. Arturo Realyvásquez-Vargas: Methodology, Validation, Investigation, Resources, Writing – original draft, Visualization.

Medinformatics
Tecnológico Nacional de México (MX), Instituto Politécnico Nacional (MX)
Openalex Percentile: Top 68%
Artificial Intelligence in Healthcare
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