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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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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
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...
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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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Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

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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,...
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Prediction Intervals01:03

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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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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Heart Failure IV: Classification and Diagnostic Evaluation01:30

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Related Experiment Video

Updated: Sep 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Risk prediction of cardiovascular disease using machine learning classifiers.

Madhumita Pal1, Smita Parija1, Ganapati Panda1

  • 1Department of Electronics and Communication Engineering, C. V. Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India.

Open Medicine (Warsaw, Poland)
|July 8, 2022
PubMed
Summary

Early detection of cardiovascular disease (CVD) is crucial. This study shows the Multi-layer Perceptron (MLP) machine learning model achieved 82.47% accuracy for automatic CVD detection, outperforming K-Nearest Neighbour (K-NN).

Keywords:
K-nearest neighbourcardiovascular diseasemachine learning algorithmsmulti-layer perceptron

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Cardiology

Background:

  • Cardiovascular disease (CVD) poses significant health risks, including mortality and disability.
  • Existing methods for CVD detection require improvement in performance and reliability.
  • Early and automated detection systems are vital for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning models for automatic cardiovascular disease detection.
  • To compare the performance of Multi-layer Perceptron (MLP) and K-Nearest Neighbour (K-NN) algorithms in CVD classification.
  • To optimize model performance through data preprocessing techniques.

Main Methods:

  • Utilized publicly available University of California Irvine repository data for cardiovascular disease detection.
  • Applied two machine learning techniques: Multi-layer Perceptron (MLP) and K-Nearest Neighbour (K-NN).
  • Implemented data cleaning by removing outliers and attributes with null values to enhance model performance.

Main Results:

  • The Multi-layer Perceptron (MLP) model achieved a detection accuracy of 82.47%.
  • The MLP model demonstrated a superior area-under-the-curve (AUC) value of 86.41% compared to the K-NN model.
  • Data preprocessing, including outlier and null value removal, significantly improved model performance.

Conclusions:

  • The Multi-layer Perceptron (MLP) model is recommended for reliable automatic cardiovascular disease detection.
  • The proposed machine learning methodology shows potential for application in detecting other diseases.
  • Further validation of the MLP model using diverse datasets is suggested for broader applicability.