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Hypertension Detection Using Explainable Stacked Ensemble Machine Learning From Clinical and Physiological Data: A
Shah Muhammad Azmat Ullah1, A B M Aowlad Hossain1, Md Ebtidaul Karim1
1Department of Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh.
Health Science Reports
|July 3, 2026
Summary
This study developed a stacked ensemble machine learning model for early hypertension detection. The AI model accurately predicts high blood pressure using patient clinical and physiological data, aiding proactive healthcare interventions.
Area of Science:
- Cardiovascular Disease Research
- Artificial Intelligence in Healthcare
- Machine Learning for Predictive Analytics
Background:
- Hypertension is a prevalent and life-threatening cardiovascular disease globally.
- Early prediction of hypertension is crucial for patient alerting and intervention.
- Leveraging artificial intelligence and machine learning can enhance hypertension detection capabilities.
Purpose of the Study:
- To develop and evaluate an automated system for hypertension detection using machine learning.
- To compare the performance and explainability of an ensemble model against existing methods.
- To investigate the impact of clinical and physiological data on hypertension prediction accuracy.
Main Methods:
- A stacked ensemble learning model was proposed, combining K-Nearest Neighbor, Random Forest, and Light Gradient Boosting Machine classifiers with a Support Vector Machine meta-classifier.
- A large dataset of 21,613 patients, including clinical and physiological data, was utilized.
- Techniques such as Synthetic Minority Oversampling Technique Tomek Link (SMOTE-Tomek) and feature selection were employed to address data imbalance and optimize performance.
Main Results:
- The proposed stacked ensemble model achieved superior accuracy in detecting hypertension compared to alternative models.
- Accuracies of 85.90%, 86.72%, and 85.91% were reported for different feature set sizes using combined clinical and physiological data.
- For clinical data alone, the model achieved up to 89.58% accuracy on specific datasets, demonstrating robust performance.
Conclusions:
- The developed model shows significant potential for early and accurate hypertension detection in clinical practice.
- The research highlights the effectiveness of AI-driven solutions in revolutionizing healthcare and predictive analytics for cardiovascular diseases.
- Timely detection through advanced machine learning can reduce individual health risks and enable proactive medical interventions.
Related Concept Videos
01:30Hypertension III: Clinical Manifestations and Diagnostic Studies
Hypertension is asymptomatic and also referred to as the "silent killer" until it progresses to a severe stage or causes target organ disease. Patients may experience symptoms stemming from the strain on blood vessels and tissues in various organs or the heart's increased workload.Physical exams might show no abnormalities other than high blood pressure. Signs of vascular damage, when present, correspond to the organs supplied by the affected vessels, leading to target organ damage. For...
01:25Errors occurring during blood pressure monitoring
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
Several factors...
01:28Hypertension I: Introduction
Hypertension is a widespread, long-term medical condition where blood pressure in the arteries remains elevated. It is characterized by systolic blood pressure readings of 130 mm Hg or above or diastolic blood pressure (DBP) readings of 80 mm Hg or higher. Unmanaged hypertension poses significant health risks, making the distinction between primary (or essential) hypertension and secondary hypertension crucial, as their management and implications vary.Primary HypertensionPrimary hypertension,...
01:29Hypertension II: Pathophysiology
Hypertension is a chronic condition in which the blood's force against artery walls is excessively high, posing risks such as heart disease. The condition's underlying mechanisms involve complex interactions among the cardiovascular, kidney, and autonomic nervous systems.Renin-Angiotensin-Aldosterone System (RAAS): This system significantly influences blood pressure regulation. When blood pressure decreases, the kidneys secrete renin. This enzyme transforms angiotensinogen, a plasma protein,...
01:30Equipments Used To Measure Blood Pressure
Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
01:18Hypertension and Regulation of Blood Pressure
Hypertension, the most common cardiovascular disease, is diagnosed through repeated measurements of elevated blood pressure. Its risks, including damage to the kidney, heart, and brain, are directly proportional to blood pressure levels. Starting from 115/75 mm Hg, the risk of cardiovascular disease doubles with each increment of 20/10 mm Hg. The diagnosis relies on blood pressure measurements, not on patient symptoms, as hypertension is often asymptomatic until end-organ damage is imminent or...