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

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.
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Neural Regulation of Blood Pressure01:18

Neural Regulation of Blood Pressure

The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
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

Comparing the Recurrence Patterns of Reduced-Margins vs. RTOG-Protocol in Adjuvant Chemoradiation of High-Grade Gliomas: A Multicenter, Open-Label, Randomized Controlled Trial.

Asian Pacific journal of cancer prevention : APJCP·2026
Same author

A machine learning-based framework for predicting hypertension using serum hematological factors.

Scientific reports·2026
Same author

Uric Acid and Uric Acid Index in Predicting Coronary Artery Disease, Cerebrovascular Events, and Mortality: A Sex-Stratified Cohort Study.

Clinical cardiology·2026
Same author

Dietary Index for Gut Microbiota and the Odds of Metabolic Dysfunction-Associated Fatty Liver Disease in Overweight and Obese Children and Adolescents: A Cross-Sectional Study.

Health science reports·2026
Same author

A Novel Machine-Learning Based Method for Resolving Secondary Structure Topology in Medium-Resolution Cryo-EM Density Maps.

International journal of molecular sciences·2026
Same author

Investigating Novel Inflammatory Indices and Their Links to Mortality, Cancer, and Cardiovascular Disease: A 10-Year Cohort Study.

Health science reports·2026

Related Experiment Videos

A Comparison of Machine Learning Algorithms for Predicting Hypertension Incidence Based on Cohort Study.

Somayeh Ghiasi1, Susan Darroudi2, Mina Moradi3

  • 1Department of Biostatistics, Faculty of Health, Mashhad University of Medical Sciences, Mashhad, Iran.

Endocrinology, Diabetes & Metabolism
|May 10, 2026
PubMed
Summary

Machine learning, specifically XGBoost, accurately predicted hypertension (HTN) development. Key risk factors identified include age, copper, BMI, triglycerides, HDL, glucose, and uric acid for better HTN prevention.

Keywords:
hypertensionmachine learningrisk factors

Related Experiment Videos

Area of Science:

  • Cardiovascular disease research
  • Biostatistics and Machine Learning applications

Background:

  • Hypertension (HTN) remains a significant global health concern, necessitating improved prediction and prevention strategies.
  • Traditional risk factor assessment may not fully capture the complexity of HTN development.

Purpose of the Study:

  • To identify key hypertension risk factors using machine learning (ML) models.
  • To enhance the accuracy of hypertension prediction through advanced computational methods.

Main Methods:

  • Analysis of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort data over 10 years.
  • Application of five ML algorithms: K-nearest neighbors (KNN), logistic regression (LR), XGBoost (XGB), random forest (RF), and neural networks (NN).
  • Identification of influential risk factors using SHAP feature importance analysis.

Main Results:

  • The XGBoost model demonstrated superior performance in predicting HTN incidence, achieving an AUC-ROC of 0.79 and 74% accuracy.
  • Key predictors consistently identified by ML models were age, copper, body mass index (BMI), triglycerides, HDL, glucose, and uric acid.
  • The XGBoost model effectively predicted HTN development over a 10-year follow-up period.

Conclusions:

  • Machine learning models, particularly XGBoost, offer a powerful tool for predicting hypertension.
  • Age, copper levels, BMI, lipid profiles (triglycerides, HDL), glucose, and uric acid are significant modifiable and non-modifiable risk factors for hypertension.
  • Integrating ML into hypertension prediction and prevention strategies is crucial for public health.