Key result
Machine learning models identify underdiagnosed hypertension with moderate accuracy reaching ~0.73 AUC.
Why the study?
Many individuals with elevated blood pressure remain undiagnosed, and identifying phenotypes linked to underdiagnosed hypertension can enhance targeted screening efforts.
Do machine learning models using demographic, socioeconomic, and cardiometabolic variables accurately identify underdiagnosed hypertension in adults with elevated blood pressure?
Cross-Sectional (n=2,465)
Yes
Do machine learning models using demographic, socioeconomic, and cardiometabolic variables accurately identify underdiagnosed hypertension in adults with elevated blood pressure?
Nearly half of US adults with elevated blood pressure are underdiagnosed, and machine learning models utilizing routine cardiometabolic and demographic data can moderately predict underdiagnosis to inform targeted screening.
Background Hypertension remains a leading modifiable risk factor for cardiovascular disease, yet many individuals with elevated blood pressure remain undiagnosed. Identifying phenotypes linked to underdiagnosis can enhance targeted screening efforts. Methods We analyzed NHANES data from August 2021 to August 2023, selecting adults with measured elevated blood pressure (≥130/80 mmHg). Underdiagnosis was defined as the absence of a self-reported hypertension diagnosis or antihypertensive treatment. Demographic, socioeconomic, and cardiometabolic variables—including BMI, HbA1c, lipid profile, and albumin-to-creatinine ratio—were incorporated with phlebotomy weights (WTPH2YR) for national representation. Data preprocessing involved imputation, standardization, and a stratified 70/30 train-test split. Machine learning models (logistic regression, random forest, HistGradientBoosting, XGBoost) were evaluated using survey-weighted AUC with 95% confidence intervals. SHAP analysis identified key predictors. Results Among 2,465 adults with hypertension, 1,158 (47.0%) were underdiagnosed. Logistic regression achieved the highest predictive performance (weighted AUC: 0.731), followed by random forest (0.729) and XGBoost (0.725). At a 0.50 threshold, the logistic model showed weighted precision of 0.689, recall of 0.672, and F1-score of 0.681. SHAP analysis ranked predictors by importance: Age, Total Body Weight, BMI, Total Cholesterol, and Albumin-to-Creatinine Ratio. Conclusion Nearly half of adults with elevated blood pressure were underdiagnosed, with younger individuals exhibiting obesity and adverse metabolic profiles disproportionately affected. Elevated BMI and cholesterol increased the risk of underdiagnosis, while a higher albumin-to-creatinine ratio correlated with reduced underdiagnosis, likely reflecting increased clinical attention in renal risk patients. These findings support the use of machine learning to inform data-driven screening strategies for early hypertension detection and prevention of secondary complications.
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Polana et al. (2026) conducted a cross-sectional in Hypertension (n=2,465). Machine learning models (logistic regression, random forest, HistGradientBoosting, XGBoost) using demographic, socioeconomic, and cardiometabolic variables vs. Diagnosed hypertension (presence of self-reported hypertension diagnosis or antihypertensive treatment) was evaluated on Underdiagnosed hypertension (absence of a self-reported hypertension diagnosis or antihypertensive treatment despite BP ≥130/80 mmHg) (Weighted AUC 0.731). Machine learning models identified underdiagnosed hypertension (present in 47.0% of hypertensive adults) with a maximum weighted AUC of 0.731 using logistic regression.
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