Key result
DSRegPSOP model predicts early-onset hypertension with ~81% AUC while providing interpretable risk factor relationships.
Why the study?
While machine learning models can predict hypertension with relative accuracy, their lack of interpretability limits their clinical utility.
Does DSRegPSOP provide an interpretable model for predicting early-onset hypertension in clinically healthy adults compared to state-of-the-art machine learning algorithms?
Case-Control (n=750)
No
Does DSRegPSOP provide an interpretable model for predicting early-onset hypertension in clinically healthy adults compared to state-of-the-art machine learning algorithms?
Effect estimate: AUC-ROC 81.27% (95% CI 72.33%-83.87%)
DSRegPSOP provides an interpretable and transparent mathematical model for predicting early-onset hypertension with performance comparable to complex machine learning algorithms.
May support interpretable risk equations in healthy adults; leaves open clinical use pending independent validation.
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.
No takes yet. Share an insight, caveat, or question.
Gutiérrez-Esparza et al. (2026) conducted a case-control in Early-onset hypertension (n=750). DSRegPSOP predictive model vs. Standard machine learning algorithms was evaluated on Prediction of early-onset hypertension (AUC-ROC) (AUC-ROC 81.27%, 95% CI 72.33%-83.87%). The DSRegPSOP predictive model successfully identified early-onset hypertension with an AUC-ROC of 81.27%, providing interpretable mathematical relationships for clinical and lifestyle risk factors.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: