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Related Concept Videos

Coronary Artery Disease I: Introduction01:30

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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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Coronary Artery Disease II: Pathophysiology01:26

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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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Coronary Artery Disease V: Interprofessional Care01:27

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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...
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Coronary Artery Disease III: Clinical Manifestations01:30

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Coronary Artery Disease (CAD) is a primary health risk worldwide, leading to significant morbidity and mortality. The condition arises from the buildup of atherosclerotic plaques within the coronary arteries, resulting in diminished blood supply to the heart muscle.The clinical manifestations of CAD vary widely, from asymptomatic stages to severe, life-threatening conditions. Understanding these manifestations is crucial for early diagnosis and effective management.Angina Pectoris: The Warning...
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Coronary Artery Disease IV: Preventive Measures01:26

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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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Peripheral artery disease (PAD) predominantly results from atherosclerosis, which involves the accumulation of fatty deposits, or plaques, within the walls of arteries. This causes them to narrow and harden, significantly reducing blood flow. PAD predominantly affects the legs, particularly the arteries supplying the thighs and calves. In rare cases, it may involve other arteries, including those in the arms.Etiology of PAD:The principal cause of PAD is atherosclerosis, which results from fatty...
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Machine learning algorithm-based risk prediction model of coronary artery disease.

Shaik Mohammad Naushad1,2, Tajamul Hussain3, Bobbala Indumathi4

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Early prediction of coronary artery disease (CAD) is crucial. Ensemble machine learning algorithms (EMLA) accurately predict CAD risk and stenosis, identifying key genetic and lifestyle factors.

Keywords:
Coronary artery diseaseEnsemble machine learning algorithmFolate and xenobiotic pathwaysMultifactor dimensionality reductionRecursive partitioning

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Area of Science:

  • Cardiovascular Medicine
  • Medical Informatics
  • Genetics

Background:

  • Coronary artery disease (CAD) poses a significant mortality risk.
  • Early prediction tools are essential for managing CAD burden.
  • Genetic and lifestyle factors contribute to CAD development.

Purpose of the Study:

  • To develop and compare machine learning models for predicting CAD risk and percentage of stenosis.
  • To identify key demographic, conventional, and genetic risk factors for CAD.
  • To evaluate the clinical utility of developed prediction models.

Main Methods:

  • Utilized a database of 648 subjects (364 CAD cases, 284 controls).
  • Developed prediction models using Ensemble Machine Learning Algorithms (EMLA), Multifactor Dimensionality Reduction (MDR), and Recursive Partitioning (RP).
  • Analyzed demographic, conventional, folate/xenobiotic genetic risk factors.

Main Results:

  • EMLA demonstrated superior performance in disease prediction (89.3%) and stenosis prediction (82.5%).
  • Key predictors for CAD risk include hypertension, alcohol intake, and genetic variants (e.g., cSHMT C1420T, CYP1A1 m2).
  • Xenobiotic pathway variants (CYP1A1 m2, GSTT1) were key determinants of percentage stenosis.

Conclusions:

  • EMLA offers higher predictability for both CAD risk and stenosis.
  • The developed models, particularly EMLA, show significant clinical utility.
  • Hypertension, alcohol intake, and specific genetic variants are critical in CAD prediction.