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Related Concept Videos

Coronary Artery Disease I: Introduction01:30

Coronary Artery Disease I: Introduction

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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...
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Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
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Coronary Artery Disease IV: Preventive Measures01:26

Coronary Artery Disease IV: Preventive Measures

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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...
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Cardiomyopathy III: Hypertrophic Cardiomyopathy01:29

Cardiomyopathy III: Hypertrophic Cardiomyopathy

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Hypertrophic cardiomyopathy, or HCM, is an autosomal dominant genetic disorder characterized by asymmetric left ventricular hypertrophy without ventricular dilation. It is more common in men and is typically diagnosed in young, athletic adults.EtiologyHCM is primarily genetic and is caused by mutations in genes encoding sarcomeric proteins. Researchers have identified over 1400 mutations across at least 11 different genes. Among these, the most frequently occurring mutations are found in the...
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Coronary Artery Disease II: Pathophysiology01:26

Coronary Artery Disease II: Pathophysiology

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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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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

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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
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Explainable machine learning for predicting coronary heart disease risk in patients with carotid atherosclerosis: A retrospective study with SHAP and decision curve analysis.

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Interpretable machine learning for coronary heart disease risk stratification in patients with carotid atherosclerosis: A retrospective cross-sectional study.

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Related Experiment Video

Updated: Mar 21, 2026

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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Machine Learning-Based Risk Prediction for Coronary Heart Disease Complicated by Hyperhomocysteinemia: Retrospective

Ming-Yuan Du1,2,3, Meng-Ke Lyu3, Hai-Long Liu1,2,3

  • 1Heart Center, The First Affiliated Hospital of Henan University of Chinese Medicine, National Regional (TCM) Cardiovascular Diagnosis and Treatment Center, Zhengzhou, China.

JMIR Medical Informatics
|March 19, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts coronary heart disease (CHD) risk in patients with hyperhomocysteinemia (HHcy). Age and activated partial thromboplastin time were key predictors, enabling personalized risk assessment.

Keywords:
coronary heart diseasehyperhomocysteinemiamachine learningpredictive modelretrospective study

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Last Updated: Mar 21, 2026

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Area of Science:

  • Cardiovascular Disease Research
  • Artificial Intelligence in Medicine
  • Biomarker Discovery

Background:

  • Hyperhomocysteinemia (HHcy) is an independent risk factor for coronary heart disease (CHD).
  • Predicting CHD risk in HHcy patients is challenging.
  • Accurate risk stratification is crucial for timely intervention.

Purpose of the Study:

  • Develop and validate machine learning (ML) models for CHD risk prediction in HHcy patients.
  • Identify key predictors of CHD risk using SHAP (Shapley Additive Explanation) algorithms.
  • Enhance early risk stratification and clinical decision-making for HHcy individuals.

Main Methods:

  • Retrospective study of HHcy patients' electronic medical records.
  • Development of seven ML models, including LightGBM.
  • Input variables: age, weight, hypertension, drinking history, aPTT, carotid plaque.
  • SHAP analysis for predictor interpretability.

Main Results:

  • The LightGBM model achieved an AUC of 0.807 and F1-score of 0.606.
  • SHAP analysis identified age and aPTT as most influential predictors.
  • Hypertension, weight, carotid plaque, and drinking history also contributed to risk prediction.

Conclusions:

  • The LightGBM model offers high accuracy and interpretability for CHD risk prediction in HHcy.
  • ML and interpretable AI can facilitate personalized risk assessment and intervention.
  • This approach supports proactive cardiovascular health management.