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Publications

Journal Article Epublish 1 citation

PubMed: 42311438 DOI: 10.3389/fdgth.2026.1805107

Explainable and interpretable models for predicting early-onset hypertension in the Tlalpan 2020 cohort.

Front Digit Health 8:1805107

Authors: Guadalupe Gutiérrez-Esparza, Mireya Martínez-García, Luis M Amezcua-Guerra, Martín Montes Rivera, Enrique Hernández-Lemus

Affiliations

  • "Researcher for Mexico" Program, Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI), Mexico City, Mexico.
  • Diagnostic and Treatment Services, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico.
  • Department of Immunology, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico. Dental Public Health Department, Division of Graduate Studies and Research, School of Dentistry, Universidad Nacional Autónoma de México, Mexico City, Mexico.
  • Department of Immunology, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico.
  • Research and postgraduate studies department, Universidad Politécnica de Aguascalientes, Aguascalientes, Ags. Mexico.
  • Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.

Abstract

BACKGROUND

Early-onset hypertension results from complex interactions among demographic, lifestyle, metabolic, and psychosocial factors. While machine learning models can predict hypertension with relative accuracy, their lack of interpretability limits their clinical utility.

METHODS

Using a nested case-control design based on the Tlalpan 2020 prospective cohort, a 10-year study of clinically healthy adults in Mexico City, this study applies DSRegPSOP, a symbolic regression approach, to develop interpretable mathematical models. The dataset included demographic, clinical, biochemical, lifestyle, and sleep-related variables. We addressed class imbalance using oversampling and SMOTE-based strategies, and evaluated model performance with accuracy, sensitivity, specificity, F1-score, and AUC-ROC.

RESULTS

DSRegPSOP produced compact analytical expressions with predictive performance comparable to state-of-the-art machine learning algorithms while preserving interpretability. The resulting models reveal clinically meaningful predictors of early-onset hypertension.

CONCLUSION

DSRegPSOP provides a transparent and interpretable model for hypertension risk assessment that shows promising potential to support early prevention strategies, pending external validation on independent cohorts.

MeSH

Hypertension, Life Style, Machine Learning, Learning, Sedentary Behavior, Demography