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

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

782
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
782

You might also read

Related Articles

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

Sort by
Same author

Predicting the Risk of Cardiovascular Diseases in the Elderly Based on Clinical Data and Heart Rate Variability Using Machine Learning.

Journal of clinical medicine·2026
Same author

Incidence trends of aplastic anaemia in Kazakhstan: a nationwide study (2014-2024).

Frontiers in medicine·2026
Same author

Non-Criteria Antiphospholipid Antibodies in Women with Recurrent Pregnancy Loss.

Biomedicines·2026
Same author

Epidemiology, Risk Factors, Diagnosis, and Comorbidities of Endometriosis: An Umbrella Review.

Journal of clinical medicine·2026
Same author

Cellular Senescence in Idiopathic Pulmonary Fibrosis: Molecular Mechanisms, Pathogenic Networks, and Emerging Therapeutic Targets.

Diseases (Basel, Switzerland)·2026
Same author

Airway Mucosal Defense: Mucins, Innate Immunity, and Contemporary Mucoactive Strategies.

Biomedicines·2026

Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

Multidimensional Visualization and AI-Driven Prediction Using Clinical and Biochemical Biomarkers in Premature

Kuat Abzaliyev1, Madina Suleimenova2, Symbat Abzaliyeva2

  • 1Department of Internal Medicine, Faculty of Medicine and Healthcare, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.

Biomedicines
|October 29, 2025
PubMed
Summary

This study integrates multidimensional data and AI to identify cardiovascular disease (CVD) risk factors, successfully separating high-risk from low-risk individuals. Findings highlight renal function and hypertension as key predictors for precision prevention.

Keywords:
biomarkersmachine learningparallel coordinatespremature agingprincipal component analysisrandom forestt-SNE

More Related Videos

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K

Related Experiment Videos

Last Updated: Jan 13, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K

Area of Science:

  • Cardiovascular Research
  • Artificial Intelligence in Medicine
  • Biomarker Discovery

Background:

  • Cardiovascular diseases (CVDs) are the leading cause of global mortality.
  • Hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) form a continuum of CVD.
  • Existing research often overlooks integrated multidimensional data and AI for pattern discovery.

Purpose of the Study:

  • To integrate clinical, biochemical, and lifestyle data for CVD risk profiling.
  • To apply multidimensional visualization and AI to identify hidden patterns and risk clusters.
  • To develop interpretable models for precision cardiovascular prevention.

Main Methods:

  • Analysis of 106 patients with integrated clinical, biochemical (renal function, inflammatory markers, lipids), and lifestyle data.
  • Application of correlation analysis, parallel coordinates, t-SNE with k-means clustering, PCA, and Random Forest with SHAP interpretation.
  • Bootstrap resampling for confidence intervals of SHAP values to assess feature stability.

Main Results:

  • t-SNE clustering achieved complete separation of high-risk (100% CVD-positive) and low-risk (7.8% CVD rate) groups.
  • Random Forest identified renal function, hypertension, and physical activity as dominant predictors (Accuracy 0.818, AUC-ROC 0.854).
  • SHAP analysis highlighted arterial hypertension, BMI, physical inactivity, renal biomarkers, and NT-proBNP as key predictors.

Conclusions:

  • Pioneered integrated multidimensional visualization and AI for interpretable CVD risk profiling.
  • Demonstrated the potential for data-driven identification of high- and low-risk clusters.
  • Findings suggest potential for interpretable models in precision prevention and decision support for cardiovascular aging.