| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607880 |
| ISSN: | 1405-5546 |
| Autores: | Maldonado Sifuentes, Christian E.1 Vargas Santiago, Mariano1 León Velasco, Diana A.2 Ortega García, M. Cristina3 Ledo Mezquita, Yoel2 Castillo Velásquez, Francisco A.4 |
| Instituciones: | 1Consejo Nacional de Humanidades Ciencia y Tecnología, México 2Instituto Tecnológico y de Estudios Superiores de Monterrey, México 3Transdisciplinary Research for Augmented Innovation Laboratory, México 4Universidad Politécnica de Querétaro, México |
| Año: | 2024 |
| Periodo: | Ene-Mar |
| Volumen: | 28 |
| Número: | 1 |
| Paginación: | 5-18 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | In this paper, we present an in-depth analysis leveraging several renowned machine learning techniques, including Snap Random Forest, XGBoost, Extra Trees, and Snap Decision Trees, to characterize comorbidity factors influencing the Mexican population. Distinct from existing literature, our study undertakes a comprehensive exploration of algorithms within a defined search space, conducting experiments ranging from coarse to fine granularity. This approach, coupled with machine learning-driven feature enhancement, enables us to deeply characterize the factors most significantly affecting COVID-19 mortality rates within the Mexican demographic. Contrary to other studies, which obscure the identification of primary factors for local populations, our findings reveal that geographical factors such as residence location hold greater significance than even comorbidities, indicating that socioeconomic factors play a pivotal role in the survival outcomes of the Mexican population. This research not only contributes to the targeted understanding of COVID-19 mortality drivers in Mexico but also highlights the critical influence of socioeconomic determinants, offering valuable insights for public health strategies and policy formulation. |
| Keyword: | Diabetes, COVID-19, Machine learning, SARS CoV-2, Cox, RMST |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
