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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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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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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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Atherosclerosis II: Clinical Manifestations and Diagnostic Tests01:27

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Atherosclerosis is a progressive disorder that leads to the thickening and narrowing of arterial walls due to plaque buildup. This condition can cause various symptoms depending on the arteries affected:Coronary Artery Disease (CAD): This condition affects the coronary arteries and may lead to chest pain (angina), shortness of breath (dyspnea), heart attacks, and other heart disease symptoms.Cerebrovascular Disease: This affects blood flow to the brain, causing transient ischemic attacks (TIAs)...
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Coronary Artery Disease V: Interprofessional Care01:27

Coronary Artery Disease V: Interprofessional Care

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

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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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Machine Learning Predictive Models for Coronary Artery Disease.

L J Muhammad1, Ibrahem Al-Shourbaji2, Ahmed Abba Haruna3

  • 1Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria.

SN Computer Science
|June 28, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict coronary artery disease (CAD) in Nigeria. The random forest model showed the highest accuracy (92.04%) and ROC (92.20%), offering potential for an expert diagnostic system.

Keywords:
CADDiseaseMachine learningPredictive model

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Cardiology

Background:

  • Coronary artery disease (CAD) is a leading cause of death globally, particularly in developing nations like Nigeria.
  • Despite its prevalence, CAD is often underdiagnosed in Nigeria, contributing to significant mortality rates, especially in individuals under 70.
  • In 2014, CAD accounted for 2.82% of all deaths in Nigeria, highlighting an urgent need for improved diagnostic tools.

Purpose of the Study:

  • To develop and evaluate machine learning predictive models for diagnosing coronary artery disease (CAD) using a Nigerian dataset.
  • To identify the most effective machine learning algorithms for CAD prediction based on accuracy, specificity, sensitivity, and ROC analysis.
  • To explore the potential of the best-performing model in developing an expert system for CAD diagnosis in Nigeria.

Main Methods:

  • A diagnostic CAD dataset was collected from two General Hospitals in Kano State, Nigeria.
  • Several machine learning algorithms, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest, Naïve Bayes, Gradient Boosting, and Logistic Regression, were applied.
  • Model performance was rigorously evaluated using accuracy, specificity, sensitivity, and Receiver Operating Characteristic (ROC) curve analysis.

Main Results:

  • The Random Forest model achieved the highest accuracy at 92.04% and the best ROC performance at 92.20%.
  • The Naïve Bayes model demonstrated the highest specificity, reaching 92.40%.
  • The Support Vector Machine model yielded the highest sensitivity at 87.34%.

Conclusions:

  • Machine learning, particularly the Random Forest algorithm, shows significant promise for accurate CAD prediction in the Nigerian context.
  • The Random Forest model's decision tree can be translated into production rules, facilitating the development of an expert system for CAD diagnosis.
  • Implementing such a system could significantly improve early detection and management of CAD patients in Nigeria, thereby reducing mortality.