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Cardiac arrest prediction in smokers using enhanced Artificial Bee Colony algorithm with stacked autoencoder model.

Umera Banu1, Dr Kalpana Vanjerkhede2

  • 1Department of Biomedical Engineering, Khaja Bandanawaz College of Engineering, Kalaburagi, India.

Computer Methods in Biomechanics and Biomedical Engineering
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Summary

This study predicts cardiac arrest in smokers using heart rate variability (HRV) and deep learning. An enhanced Artificial Bee Colony algorithm (EABC) and stacked autoencoder achieved 96.26% accuracy for early diagnosis.

Keywords:
Artificial Bee Colony algorithmcardiac arrest predictionheart rate variabilitymachine learningstacked autoencoder model

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Cardiac arrest is a leading cause of mortality globally.
  • Early prediction of cardiac arrest is crucial for timely intervention.
  • Smokers represent a high-risk group for cardiovascular diseases.

Purpose of the Study:

  • To develop a deep learning model for predicting cardiac arrest in smokers.
  • To utilize heart rate variability (HRV) parameters for risk assessment.
  • To enhance feature selection and classification accuracy for early diagnosis.

Main Methods:

  • Collected data from the MITU Skillogies dataset (1562 instances).
  • Employed an enhanced Artificial Bee Colony algorithm (EABC) for feature selection.
  • Utilized a stacked autoencoder classifier for cardiac arrest prediction.

Main Results:

  • The EABC algorithm effectively reduced the number of HRV attributes.
  • The EABC with stacked autoencoder model achieved 96.26% classification accuracy.
  • This model outperformed traditional machine learning approaches.

Conclusions:

  • Deep learning models, particularly EABC with stacked autoencoder, show high efficacy in predicting cardiac arrest in smokers.
  • HRV analysis combined with advanced algorithms offers a promising tool for early cardiac arrest detection.
  • The proposed method reduces computational complexity and improves diagnostic accuracy.