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A new stacking ensemble model using deep neural networks accurately predicts heart disease in COVID-19 survivors. This advancement aids in managing long COVID complications and improving patient outcomes.

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Area of Science:

  • Cardiology
  • Infectious Diseases
  • Artificial Intelligence in Medicine

Background:

  • The COVID-19 pandemic has led to widespread healthcare challenges.
  • Long COVID complications, particularly heart disease, strain healthcare resources.
  • Limited datasets on post-COVID-19 complications hinder research.

Purpose of the Study:

  • To develop a predictive model for heart disease in COVID-19 survivors.
  • To address the scarcity of post-COVID-19 complication data.
  • To evaluate the model's performance against established techniques.

Main Methods:

  • Collected data from COVID-19 survivors regarding post-COVID complications.
  • Preprocessed data, handled missing values, and applied oversampling.
  • Developed a stacking ensemble binary classifier with deep neural networks.

Main Results:

  • The proposed model achieved 93.23% accuracy in predicting heart disease.
  • Demonstrated high specificity (95.74%), precision (95.24%), and recall (92.05%).
  • Outperformed baseline models including decision trees, random forest, SVM, and ANN.

Conclusions:

  • The developed stacking ensemble model is effective for predicting post-COVID-19 heart disease.
  • The approach offers a valuable tool for managing long COVID cardiac complications.
  • This research contributes to understanding and mitigating long-term effects of COVID-19.