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Coronary Artery Disease I: Introduction01:30

Coronary Artery Disease I: Introduction

42
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...
42
Coronary Artery Disease IV: Preventive Measures01:26

Coronary Artery Disease IV: Preventive Measures

28
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...
28
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

55
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Coronary Artery Disease II: Pathophysiology01:26

Coronary Artery Disease II: Pathophysiology

20
Coronary Artery Disease (CAD) originates from a series of events that impair the function of coronary arteries, the blood vessels responsible for delivering oxygen-rich blood to the heart muscle. The pathophysiology of CAD is closely linked to atherosclerosis, a chronic inflammatory and lipid-driven condition affecting the vascular endothelium.1. Endothelial DamageThe process begins with damage to the vascular endothelium, which serves as a protective barrier between the blood and the vessel...
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Coronary Artery Disease V: Interprofessional Care01:27

Coronary Artery Disease V: Interprofessional Care

21
Interprofessional care for coronary artery disease includes pharmacological therapy and revascularization procedures.Pharmacological therapy for Coronary Artery Disease (CAD) aims to manage symptoms, prevent complications, and improve patient outcomes through various classes of medications:Antiplatelet Agents:Aspirin and Clopidogrel: These medications inhibit platelet aggregation, preventing blood clots, which is crucial for avoiding heart attacks and strokes. Doctors often prescribe these...
21
Acute Coronary Syndrome III: Diagnostic Studies01:30

Acute Coronary Syndrome III: Diagnostic Studies

19
Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Related Experiment Video

Updated: Aug 10, 2025

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

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Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models.

Maria Trigka1, Elias Dritsas1

  • 1Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances coronary artery disease (CAD) risk prediction using machine learning. A stacking ensemble model with synthetic minority oversampling technique (SMOTE) achieved 90.9% accuracy, outperforming other methods for long-term CAD risk assessment.

Keywords:
coronary artery diseasefeature analysishealthcarelong-term risk predictionmachine learning

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Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Coronary artery disease (CAD) arises from atherosclerotic plaques narrowing heart arteries, obstructing blood flow.
  • Early detection and prevention are crucial for managing this life-threatening condition.
  • Accurate long-term risk prediction is essential for timely intervention and patient management.

Purpose of the Study:

  • To evaluate and compare various machine learning (ML) models for long-term coronary artery disease (CAD) risk prediction.
  • To assess the impact of the synthetic minority oversampling technique (SMOTE) on model performance.
  • To identify the optimal ML model for predicting CAD risk.

Main Methods:

  • Experimentation with multiple machine learning (ML) models.
  • Application and evaluation of the synthetic minority oversampling technique (SMOTE).
  • Utilizing 10-fold cross-validation to assess model accuracy, precision, recall, and Area Under the Curve (AUC).

Main Results:

  • The stacking ensemble model, combined with SMOTE and 10-fold cross-validation, demonstrated superior performance.
  • This model achieved an accuracy of 90.9%, precision of 96.7%, recall of 87.6%, and an AUC of 96.1%.
  • The results indicate the effectiveness of SMOTE in improving ML model performance for CAD risk prediction.

Conclusions:

  • Machine learning models, particularly the stacking ensemble with SMOTE, show significant promise for accurate long-term coronary artery disease risk prediction.
  • The findings suggest that SMOTE can enhance the predictive power of ML models in this domain.
  • This approach offers a valuable tool for improving cardiovascular health outcomes through early risk identification.