Machine learning predictive model to identify metabolic status in Mexican children, using homeostasis model assessment insulin resistance and amylase enzymatic activity
It is concluded that with these variables and RF, it is feasible to have a prediction model that contributes to identifying this type of patients in the prepathogenic period.
Authors
- Erika Acosta-Cruz (ORCID: https://orcid.org/0000-0002-7881-3998)
- Karen E. Villagrana-Bañuelos (ORCID: https://orcid.org/0000-0002-0684-6483)
- Miguel Vázquez‐Moreno (ORCID: https://orcid.org/0000-0002-9170-0488)
- Carlos E. Galván-Tejada (ORCID: https://orcid.org/0000-0002-7635-4687)
- Antonio García-Domínguez (ORCID: https://orcid.org/0000-0001-9538-2029)
- Miguel Cruz (ORCID: https://orcid.org/0000-0001-9985-6172)
Institutions
- Mexican Social Security Institute (MX)
- Universidad Autónoma de Coahuila (MX)
- Centro Medico Nacional Siglo XXI (MX)
- Hospital de Especialidades (MX)
- Universidad Autónoma de Zacatecas "Francisco García Salinas" (MX)
Publication Details
- Journal
- Gaceta Médica de México
- Published
- 2025-10-08
- DOI
- https://doi.org/10.24875/gmm.24000401
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00