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Related Concept Videos

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Cardiomyopathy, or CMP, is a group of diseases affecting the myocardial structure, impairing its ability to pump blood effectively. This condition can lead to arrhythmias, heart failure, or sudden cardiac death.Cardiomyopathies are classified into primary and secondary categories:Primary Cardiomyopathy refers to conditions involving only the heart muscle that are often idiopathic (of unknown cause) or genetic. They primarily affect the myocardium without the involvement of other systemic...
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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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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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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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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Related Experiment Video

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In Silico Clinical Trials for Cardiovascular Disease
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A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques.

Ankur Kumar1, Sanjay Dhanka2, Abhinav Sharma2

  • 1School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, Mandi, 175005, Himachal Pradesh, India.

Scientific Reports
|July 11, 2025
PubMed
Summary

This study introduces a hybrid framework combining classical and quantum-inspired machine learning for improved heart disease prediction. The novel approach enhances prediction accuracy and robustness using integrated datasets and advanced optimization techniques.

Keywords:
Arrhythmia classificationFeature selectionMachine learningParticle swarm optimizationPearson’s correlation coefficient

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Area of Science:

  • Cardiovascular Health
  • Machine Learning
  • Computational Science

Background:

  • Accurate heart disease prediction is crucial for timely intervention and improved patient outcomes.
  • Existing machine learning models face challenges in prediction accuracy and robustness.
  • Integrating diverse datasets and advanced computational techniques can potentially overcome these limitations.

Purpose of the Study:

  • To propose and evaluate a novel hybrid framework for heart disease prediction.
  • To integrate classical and quantum-inspired machine learning techniques for enhanced performance.
  • To compare the proposed framework against state-of-the-art methods.

Main Methods:

  • A hybrid framework combining classical and quantum-inspired machine learning models was developed.
  • Data from multiple heart disease datasets (Cleveland, Hungarian, Switzerland, Long Beach, Statlog) were combined and preprocessed.
  • Support Vector Machine (SVM) classifiers were trained and optimized using Genetic Algorithms (CGA), Particle Swarm Optimization (CPSO), Quantum Genetic Algorithms (QGAs), and Quantum Particle Swarm Optimization (QPSO).
  • Tenfold cross-validation was used to assess performance using various metrics including accuracy, F1-score, precision, sensitivity, specificity, and diagnostic odds ratio (DOR).

Main Results:

  • The hybrid framework demonstrated improved heart disease prediction accuracy and robustness compared to existing methods.
  • Both classical and quantum-inspired models showed competitive performance, with the hybrid approach offering enhanced capabilities.
  • Feature selection and rigorous cross-validation ensured reliable model evaluation.

Conclusions:

  • The proposed hybrid framework integrating classical and quantum-inspired machine learning shows significant potential for advancing heart disease prediction.
  • This novel approach offers a robust and accurate method for identifying individuals at risk of heart disease.
  • Further research can explore broader applications of this hybrid framework in clinical settings.