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

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

  • By

  • Guadalupe Gutiérrez-Esparza

  • Mireya Martínez-García

  • Luis M. Amezcua-Guerra

  • Martín Montes Rivera

  • Enrique Hernández-Lemus

  • June 2, 2026

  • 0 min

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Objective:

To develop interpretable mathematical models for predicting early-onset hypertension using a symbolic regression approach.

Key Findings:
  • DSRegPSOP produced compact analytical expressions with predictive performance comparable to state-of-the-art machine learning algorithms.
  • The models revealed clinically meaningful predictors of early-onset hypertension.
Interpretation:

DSRegPSOP provides a transparent and interpretable model for hypertension risk assessment.

Limitations:
  • The findings require external validation on independent cohorts.
Conclusion:

DSRegPSOP shows promising potential to support early prevention strategies for hypertension.

Original Source(s)

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

  • by Guadalupe Gutiérrez-Esparza, Mireya Martínez-García, Luis M. Amezcua-Guerra, Martín Montes Rivera, Enrique Hernández-Lemus

  • June 2, 2026

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