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Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

Author

Listed:
  • Tatjana Gligorijević
  • Zoran Ševarac
  • Branislav Milovanović
  • Vlado Đajić
  • Marija Zdravković
  • Saša Hinić
  • Marina Arsić
  • Milica Aleksić

Abstract

Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 -measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.

Suggested Citation

  • Tatjana Gligorijević & Zoran Ševarac & Branislav Milovanović & Vlado Đajić & Marija Zdravković & Saša Hinić & Marina Arsić & Milica Aleksić, 2017. "Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks," Complexity, Hindawi, vol. 2017, pages 1-8, September.
    • Handle: RePEc:hin:complx:8953083
    DOI: 10.1155/2017/8953083
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    Cited by:

    1. Federico Nuñez-Piña & Joselito Medina-Marin & Juan Carlos Seck-Tuoh-Mora & Norberto Hernandez-Romero & Eva Selene Hernandez-Gress, 2018. "Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks," Complexity, Hindawi, vol. 2018, pages 1-10, January.

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