Factors Associated with COVID-19 Mortality in Mexico: A Machine Learning Approach Using Clinical, Socioeconomic, and Environmental Data
Saved in:
| Published in: | Machine Learning and Knowledge Extraction vol. 7, no. 2 (2025), p. 55-95 |
|---|---|
| Main Author: | |
| Other Authors: | , , |
| Published: |
MDPI AG
|
| Subjects: | |
| Online Access: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3223924824 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-4990 | ||
| 024 | 7 | |a 10.3390/make7020055 |2 doi | |
| 035 | |a 3223924824 | ||
| 045 | 2 | |b d20250401 |b d20250630 | |
| 100 | 1 | |a Díaz-González Lorena |u Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico | |
| 245 | 1 | |a Factors Associated with COVID-19 Mortality in Mexico: A Machine Learning Approach Using Clinical, Socioeconomic, and Environmental Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a COVID-19 mortality is a complex phenomenon influenced by multiple factors. This study aimed to identify factors associated with death in COVID-19 patients by considering clinical, demographic, environmental, and socioeconomic conditions, using machine learning models and a national dataset from Mexico covering all pandemic waves. We integrated data from the national COVID-19 dataset, municipal-level socioeconomic indicators, and water quality contaminants (physicochemical and microbiological). Patients were assigned to one of four datasets (groundwater, lentic, lotic, and coastal) based on their municipality of residence. We trained XGBoost models to predict patient death or survival on balanced subsets of each dataset. Hyperparameters were optimized using a grid search and cross-validation, and feature importance was analyzed using SHAP values, point-biserial correlation, and XGBoost metrics. The models achieved strong predictive performance (F1 score > 0.97). Key risk factors included older age (≥50 years), pneumonia, intubation, obesity, diabetes, hypertension, and chronic kidney disease, while outpatient status, younger age (<40 years), contact with a confirmed case, and care in private medical units were associated with survival. Female sex showed a protective trend. Higher socioeconomic levels appeared protective, whereas lower levels increased risk. Water quality contaminants (e.g., manganese, hardness, fluoride, dissolved oxygen, fecal coliforms) ranked among the top 30 features, suggesting an association between environmental factors and COVID-19 mortality. | |
| 651 | 4 | |a Mexico | |
| 653 | |a Datasets | ||
| 653 | |a Coliforms | ||
| 653 | |a Contaminants | ||
| 653 | |a Body mass index | ||
| 653 | |a Human Development Index | ||
| 653 | |a Mortality | ||
| 653 | |a Survival | ||
| 653 | |a Dissolved oxygen | ||
| 653 | |a Databases | ||
| 653 | |a Kidney diseases | ||
| 653 | |a Water quality | ||
| 653 | |a Age | ||
| 653 | |a Machine learning | ||
| 653 | |a Manganese | ||
| 653 | |a Death | ||
| 653 | |a COVID-19 | ||
| 700 | 1 | |a Toribio-Colin, Yael Sharim |u Licenciatura en Ciencias, Instituto de Investigación en Ciencias Básicas Aplicadas (IICBA), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico; yael.toribiocol@uaem.edu.mx | |
| 700 | 1 | |a Pérez-Sansalvador, Julio César |u Department of Computer Science, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Tonantzintla 72840, Puebla, Mexico | |
| 700 | 1 | |a Lakouari Noureddine |u Department of Computer Science, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Tonantzintla 72840, Puebla, Mexico | |
| 773 | 0 | |t Machine Learning and Knowledge Extraction |g vol. 7, no. 2 (2025), p. 55-95 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223924824/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223924824/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223924824/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |