Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Factors affecting Blood pressure01:28

Factors affecting Blood pressure

Several physiological and lifestyle factors influence blood pressure (BP). Understanding these factors is crucial as they are significant in patient education and blood pressure management.
Physiological Factors:
Errors occurring during blood pressure monitoring01:25

Errors 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...
Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the patient.
Hypertension and Regulation of Blood Pressure01:18

Hypertension 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...
Hypertension I: Introduction01:28

Hypertension 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,...
Hypertension III: Clinical Manifestations and Diagnostic Studies01:30

Hypertension 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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mode of Delivery and Neonatal Characteristics as Risk Factors for Childhood Asthma in Qatar: A Case-Control Study.

Pediatric pulmonology·2026
Same author

Health and Well-Being in the Context of Health-Promoting University Initiatives: Protocol for a Mixed Methods Needs Assessment Study at Qatar University.

JMIR research protocols·2024
Same author

Comparison of intravenous paracetamol (acetaminophen) to intravenously or intramuscularly administered non-steroidal anti-inflammatory drugs (NSAIDs) or opioids for patients presenting with moderate to severe acute pain conditions to the ED: systematic review and meta-analysis.

Emergency medicine journal : EMJ·2023
Same author

Factors Influencing Public Attitudes towards COVID-19 Vaccination: A Scoping Review Informed by the Socio-Ecological Model.

Vaccines·2021
Same author

Comparing Levels of Metabolic Predictors of Coronary Heart Disease between Healthy Lean and Overweight Females.

Metabolites·2021
Same author

Early feeding practices and associated factors in Sudan: a cross-sectional analysis from multiple Indicator cluster survey.

International breastfeeding journal·2020

Related Experiment Video

Updated: Jun 21, 2026

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions
10:38

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

Published on: June 6, 2017

13.1K

Predicting hypertension using machine learning: Findings from Qatar Biobank Study.

Latifa A AlKaabi1, Lina S Ahmed1, Maryam F Al Attiyah1

  • 1Department of Public Health, College of Health Science, QU Health, Qatar University, Doha, Qatar.

Plos One
|October 16, 2020
PubMed
Summary

Machine learning models can predict hypertension risk using non-invasive factors. Random forest and logistic regression showed similar high accuracy, aiding early hypertension screening.

More Related Videos

Differentiation of Human Pluripotent Stem Cells Into Pancreatic Beta-Cell Precursors in a 2D Culture System
10:12

Differentiation of Human Pluripotent Stem Cells Into Pancreatic Beta-Cell Precursors in a 2D Culture System

Published on: December 16, 2021

3.1K
Robust Differentiation of Human iPSCs into a Pure Population of Adipocytes to Study Adipocyte-Associated Disorders
10:31

Robust Differentiation of Human iPSCs into a Pure Population of Adipocytes to Study Adipocyte-Associated Disorders

Published on: February 9, 2022

3.9K

Related Experiment Videos

Last Updated: Jun 21, 2026

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions
10:38

Measurement of the Rheology of Crude Oil in Equilibrium with CO2 at Reservoir Conditions

Published on: June 6, 2017

13.1K
Differentiation of Human Pluripotent Stem Cells Into Pancreatic Beta-Cell Precursors in a 2D Culture System
10:12

Differentiation of Human Pluripotent Stem Cells Into Pancreatic Beta-Cell Precursors in a 2D Culture System

Published on: December 16, 2021

3.1K
Robust Differentiation of Human iPSCs into a Pure Population of Adipocytes to Study Adipocyte-Associated Disorders
10:31

Robust Differentiation of Human iPSCs into a Pure Population of Adipocytes to Study Adipocyte-Associated Disorders

Published on: February 9, 2022

3.9K

Area of Science:

  • Cardiovascular disease research
  • Biomedical informatics
  • Machine learning in healthcare

Background:

  • Hypertension is a global health issue with numerous risk factors.
  • Early prediction and diagnosis are crucial for preventing severe health complications.
  • Non-invasive methods for hypertension risk assessment are highly desirable.

Purpose of the Study:

  • To develop and compare machine learning models for predicting hypertension risk.
  • To identify individuals at high risk of hypertension without invasive procedures.
  • To evaluate the performance of different predictive algorithms.

Main Methods:

  • A cross-sectional study of 987 Qataris and long-term residents aged 18+.
  • Construction and comparison of predictive models using decision tree, random forest, and logistic regression.
  • 5-fold cross-validation and performance assessment via accuracy, PPV, sensitivity, F-measure, and AUC.

Main Results:

  • Key predictors included age, gender, lifestyle factors, and medical history.
  • Random forest (82.1% accuracy) and logistic regression (81.1% accuracy) demonstrated strong predictive performance.
  • Decision tree showed lower discrimination ability (AUC 79.9) compared to random forest (AUC 86.9) and logistic regression (AUC 85.0).

Conclusions:

  • Machine learning offers a rapid, non-invasive approach for hypertension screening.
  • Models utilizing non-invasive predictors can effectively identify individuals at high risk.
  • Future research should focus on larger populations and diverse algorithms to enhance predictive accuracy.