Related Experiment Video
Updated: Jan 6, 2026
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Machine learning model for detecting masked hypertension in young adults
Brendyn Miller1, Samuel J Coeyman2, Annemarie Wentzel3,4
1Institute for Regenerative Medicine, Wake Forest University, Winston-Salem, NC, United States.
Insights
Machine learning models can predict masked hypertension (MHT) using clinical data, improving early detection. This approach aids in managing cardiovascular disease risks, especially in resource-limited settings.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- Cardiovascular disease (CVD) is a leading cause of death globally, with hypertension (HT) contributing significantly.
- Masked hypertension (MHT), normal BP in clinic but high out-of-clinic, increases CVD risk and is often undiagnosed.
- Current diagnostic tools like ABPM and HBPM have accessibility and feasibility limitations.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting MHT using single-visit clinical data.
- To address the diagnostic challenges of MHT, particularly in resource-constrained environments.
Main Methods:
- Utilized data from the African-PREDICT study, including clinical, biomarker, body composition, and physical activity metrics from a young South African cohort.
- Employed ML models, including LASSO feature selection and extreme gradient boosting, for MHT prediction.
- Evaluated model performance using accuracy and ROC AUC scores.
Main Results:
- An ML model combining LASSO feature selection and extreme gradient boosting achieved 0.83 accuracy and 0.86 ROC AUC.
- The model primarily relied on four key features: systolic blood pressure, body weight, left ventricular mass at systole, and dehydroepiandrosterone sulfate levels.
- This predictive framework demonstrates potential for early MHT identification, reducing reliance on resource-intensive monitoring.
Conclusions:
- ML-based prediction of MHT offers a feasible approach for early detection and intervention.
- This strategy can help mitigate MHT progression and associated cardiovascular risks, particularly in underserved regions.
- Integrating computational techniques into clinical practice is crucial for addressing global health challenges like hypertension.
Introduction:
Cardiovascular disease (CVD) remains the leading global cause of mortality, with hypertension (HT) being a significant contributor, responsible for 56% of CVD-related deaths. Masked hypertension (MHT), a condition where patients exhibit normotensive blood pressure (BP) in clinical settings but elevated BP in out-of-clinic measurements, poses an elevated risk for cardiovascular complications and often goes undiagnosed. Current diagnostic methods, such as ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM), have limitations in feasibility and accessibility.
Methods:
This study aimed to address these challenges by leveraging machine learning (ML) models to predict MHT based on clinical data from a single outpatient visit. Utilizing a dataset from the African-PREDICT study, which included comprehensive clinical, biomarker, body composition, and physical activity data from a young, healthy cohort (aged 20-30 years) in South Africa, we developed a predictive framework for MHT detection.
Results:
The ML models demonstrated the potential to enhance early identification and treatment of MHT, reducing reliance on resource-intensive methods like ABPM. Specifically, we found that utilizing a Least Absolute Shrinkage and Selection Operator (LASSO) feature selection method with an extreme gradient boosting model had an accuracy of 0.83 and a ROC AUC score of 0.86 while relying predominantly on four features: systolic blood pressure, body weight, left ventricular mass at systole, and circulating levels of dehydroepiandrosterone sulfate.
Discussion:
This approach could enable targeted interventions, particularly in resource-limited settings, thereby mitigating the progression of MHT and its associated risks. These findings underscore the importance of integrating advanced computational techniques into clinical practice to address global health challenges.
Related Concept Videos
01:30Hypertension III: Clinical Manifestations and Diagnostic Studies
01:28Hypertension I: Introduction
01:10Pre-Procedural Guidelines for Assessing Blood Pressure
01:25Errors occurring during blood pressure monitoring
Several factors...
01:29Hypertension II: Pathophysiology
01:23Assessment of blood pressure in brachial artery(two-step method)