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Cardiac emergencies are critical situations involving the heart that require immediate medical intervention to prevent severe complications or death. These emergencies often arise from underlying heart conditions that impair the heart's ability to function correctly.Types of Cardiac EmergenciesThe most common types of cardiac emergencies include Acute Coronary Syndrome (ACS), myocardial infarction (MI), cardiac arrest, and heart failure.Acute Coronary Syndrome (ACS)Acute Coronary Syndrome (ACS)...
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Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Krishna Kumar1, Narendra Kumar2, Aman Kumar3,4

  • 1Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India.

Computational Intelligence and Neuroscience
|August 1, 2022
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Summary
This summary is machine-generated.

Mathematical models, including artificial neural networks (ANN), can effectively identify heart disease patients. The ANN model demonstrated superior accuracy compared to curve fitting, offering a non-invasive diagnostic tool for medical professionals.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Data Science

Background:

  • Chronic diseases, particularly heart disease, pose a significant global health challenge.
  • Coronary Artery Disease (CAD) is the most prevalent form, leading to heart attacks.
  • Traditional risk factors include hypertension, high cholesterol, and smoking, but accurate risk estimation remains complex.

Purpose of the Study:

  • To develop mathematical models for identifying patients with heart disease.
  • To compare the efficacy of curve fitting and artificial neural network (ANN) models in cardiac patient identification.
  • To provide a non-invasive method for early detection of heart disease.

Main Methods:

  • Utilized a medical database of patients diagnosed with heart disease.
  • Applied curve fitting techniques to model patient data.
  • Employed artificial neural network (ANN) models for comparative analysis.
  • Evaluated model performance using metrics such as R-squared, MAE, and RMSE.

Main Results:

  • The curve fitting model achieved an R-squared value of 0.6337, MAE of 0.293, and RMSE of 0.3688.
  • The ANN-based model demonstrated higher accuracy with an R-squared value of 0.8491, MAE of 0.20, and RMSE of 0.267.
  • ANN provided superior mathematical modeling for identifying heart disease patients compared to curve fitting.

Conclusions:

  • Artificial neural networks offer a more accurate approach to modeling and identifying heart disease patients.
  • The developed ANN model can assist medical professionals in diagnosing heart conditions without invasive procedures like angiography.
  • This research highlights the potential of AI in improving cardiovascular diagnostics and patient management.