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

382
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...
382
Coronary Artery Disease I: Introduction01:30

Coronary Artery Disease I: Introduction

163
Coronary Artery Disease (CAD): An Overview with Scientific InsightsCoronary Artery Disease (CAD), often referred to as C-A-D, is a prevalent blood vessel disorder classified under the broader category of atherosclerosis. Atherosclerosis is a pathological process characterized by the hardening and narrowing of arteries due to the accumulation of atherosclerotic plaques. These plaques are composed of cholesterol, fatty substances, inflammatory cells, calcium, and fibrin, reducing blood flow to...
163
Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

3.3K
Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
3.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

586
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:
586
Coronary Artery Disease IV: Preventive Measures01:26

Coronary Artery Disease IV: Preventive Measures

113
Effective preventive measures for coronary artery disease (CAD) focus on controlling modifiable risk factors, including cholesterol abnormalities and lifestyle changes.Cholesterol ManagementFirst, the Mediterranean diet and the American Heart Association advocate for maintaining low-density lipoprotein (LDL) cholesterol levels below 100 mg/dL, with a more stringent recommendation of below 70 mg/dL for individuals at high risk. LDL cholesterol, often termed "bad cholesterol," can lead to the...
113
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

131
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
131

You might also read

Related Articles

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

Sort by
Same author

Artificial intelligence applied to post-resuscitation ECGs for early prognostication after out-of-hospital cardiac arrest.

Frontiers in cardiovascular medicine·2026
Same author

Artificial intelligence in healthcare: Proposal for a new medico-legal methodology in medical liability.

Legal medicine (Tokyo, Japan)·2025
Same author

Effect of Gamification on Task Engagement During an Eye-Tracking Test Battery in 5-Year-Old Children Born Preterm: Observational Study.

JMIR serious games·2025
Same author

Explainable machine learning and deep learning models for predicting TAS2R-bitter molecule interactions.

Journal of molecular graphics & modelling·2025
Same author

LightCPPgen: An explainable machine learning pipeline for rational design of cell penetrating peptides.

International journal of antimicrobial agents·2025
Same author

Adverse cardiovascular events in coronary Plaques not undeRgoing pErcutaneous coronary intervention evaluateD with optIcal Coherence Tomography. The PREDICT-AI risk model.

Open heart·2025

Related Experiment Video

Updated: Oct 1, 2025

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

1.5K

Cardiovascular risk prediction: from classical statistical methods to machine learning approaches.

Michela Sperti1, Marta Malavolta1, Federica Staunovo Polacco1

  • 1Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy.

Minerva Cardiology and Angiology
|March 9, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models significantly enhance cardiovascular risk prediction compared to traditional scores by capturing complex, non-linear relationships. This review compares classical and machine learning approaches for better clinical decision-making.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.7K

Related Experiment Videos

Last Updated: Oct 1, 2025

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

1.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.7K

Area of Science:

  • Cardiology
  • Medical Informatics
  • Data Science

Background:

  • Cardiovascular risk prediction scores are vital for primary and secondary prevention.
  • Traditional scores often assume linear relationships, limiting accuracy.
  • Machine learning (ML) offers potential for improved prediction by modeling non-linear data.

Purpose of the Study:

  • To review and compare classical statistical and ML-based cardiovascular risk scores.
  • To highlight the strengths and limitations of each approach for clinical application.
  • To provide physicians with a critical understanding of available risk prediction tools.

Main Methods:

  • Literature review of classical statistical and ML-based cardiovascular risk scores.
  • Comparative analysis of methodologies, accuracy, and clinical utility.
  • Discussion of non-linearity in cardiovascular risk factor modeling.

Main Results:

  • Classical scores have limitations due to linear assumptions.
  • ML techniques demonstrate superior ability to capture complex, non-linear patterns in cardiovascular data.
  • Both approaches have distinct advantages and drawbacks for risk stratification.

Conclusions:

  • ML-based scores show promise for enhancing cardiovascular risk prediction accuracy.
  • Understanding the differences between classical and ML scores is crucial for effective clinical implementation.
  • Further research is needed to optimize ML applications in cardiovascular disease prevention.