Interpretable Machine Learning for Predicting Early Onset Hypertension in the US.
- 1Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, Texas.
- 2Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station, Texas.
- 3Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California, San Francisco.
Abstract
Introduction
Early onset hypertension is often overlooked despite its association with lifelong cardiovascular disease risk. We developed an interpretable machine learning model to predict early onset hypertension in US adults using the 2022 Medical Expenditure Panel Survey, incorporating demographic, socioeconomic, health care utilization, and comorbidity features.Methods
We analyzed data on adults aged 55 years or younger. Hypertension was defined by International Classification of Diseases, 10th Revision, Clinical Modification code number I10 or self-reported physician diagnosis at or before age 55 years. Eighty-four variables were screened; Least Absolute Shrinkage and Selection Operator reduced the set to 45 variables. Extreme gradient boosting was trained with stratified 5-fold cross-validation. Performance metrics included accuracy, precision, recall, F2 score, area under the receiver operating characteristic curve (ROC AUC), and area under the precision-recall curve (PR AUC). We assessed fairness across subgroups and used Shapley Additive Explanations to quantify feature influence.Results
Among 9,850 adults, 1,647 (weighted prevalence, 15.2%) had hypertension. Discrimination was strong, with a ROC AUC of 0.807 (95% CI, 0.787-0.827), PR AUC of 0.486 (95% CI, 0.439-0.530), recall of 0.848 (95% CI, 0.816-0.878), and F2 score of 0.606 (95% CI, 0.578-0.634). Key predictors included older age, high body mass index, and a diagnosis of high cholesterol. Fairness analyses showed slightly higher true positive rates for Hispanic (+0.11) and Asian (+0.08) people, with performance for African American people modestly lower than that for White people.Conclusion
Both medical and nonmedical predictors contributed meaningfully, with high cholesterol particularly influential among adults older than 40 years and race and ethnicity especially important among African American adults. Prospective validation and routine fairness audits are warranted to ensure equitable benefit across populations. This interpretable model supports recall-oriented screening to identify younger adults at risk of hypertension for further evaluation and early intervention.
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BibTeX
@article{wu2026interpretable,
title = {{Interpretable Machine Learning for Predicting Early Onset Hypertension in the US.}},
author = {Li-Wei Wu and Kiyoung Kim and Peter Washington and Chanhyun Park},
journal = {Preventing chronic disease},
year = {2026},
month = oct,
doi = {10.5888/pcd23.260109},
url = {https://europepmc.org/article/MED/42911818},
note = {CC BY},
}
RIS
TY - JOUR
TI - Interpretable Machine Learning for Predicting Early Onset Hypertension in the US.
AU - Li-Wei Wu
AU - Kiyoung Kim
AU - Peter Washington
AU - Chanhyun Park
PY - 2026
DA - 2026/10/22
JO - Preventing chronic disease
DO - 10.5888/pcd23.260109
UR - https://europepmc.org/article/MED/42911818
AB - Introduction Early onset hypertension is often overlooked despite its association with lifelong cardiovascular disease risk. We developed an interpretable machine learning model to predict early onset hypertension in US adults using the 2022 Medical Expenditure Panel Survey, incorporating demographic, socioeconomic, health care utilization, and comorbidity features.Methods We analyzed data on adults aged 55 years or younger. Hypertension was defined by International Classification of Diseases, 10th Revision, Clinical Modification code number I10 or self-reported physician diagnosis at or before age 55 years. Eighty-four variables were screened; Least Absolute Shrinkage and Selection Operator reduced the set to 45 variables. Extreme gradient boosting was trained with stratified 5-fold cross-validation. Performance metrics included accuracy, precision, recall, F2 score, area under the receiver operating characteristic curve (ROC AUC), and area under the precision-recall curve (PR AUC). We assessed fairness across subgroups and used Shapley Additive Explanations to quantify feature influence.Results Among 9,850 adults, 1,647 (weighted prevalence, 15.2%) had hypertension. Discrimination was strong, with a ROC AUC of 0.807 (95% CI, 0.787-0.827), PR AUC of 0.486 (95% CI, 0.439-0.530), recall of 0.848 (95% CI, 0.816-0.878), and F2 score of 0.606 (95% CI, 0.578-0.634). Key predictors included older age, high body mass index, and a diagnosis of high cholesterol. Fairness analyses showed slightly higher true positive rates for Hispanic (+0.11) and Asian (+0.08) people, with performance for African American people modestly lower than that for White people.Conclusion Both medical and nonmedical predictors contributed meaningfully, with high cholesterol particularly influential among adults older than 40 years and race and ethnicity especially important among African American adults. Prospective validation and routine fairness audits are warranted to ensure equitable benefit across populations. This interpretable model supports recall-oriented screening to identify younger adults at risk of hypertension for further evaluation and early intervention.
SN - 1545-1151
ER -