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DOI: 10.14569/IJACSA.2013.040714
PDF

An Intelligent Diagnostic System for Congenital Heart Defects

Author 1: Amir Mohammad Amiri
Author 2: Giuliano Armano

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 7, 2013.

  • Abstract and Keywords
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Abstract: Congenital heart disease is the most common birth defect. The article describes detection and classification of congenital heart defect using classification and regressing trees. The ultimate goal of this research can decrease risk of cardiac arrest and mortality in compared with healthy children. The intelligent system proposed in three stages technique for automate diagnosis:(i) pre-processing(ii), feature extraction, and (iii) classification of congenital heart defects (CHD) using data mining tools. The intelligent diagnostic system has been validated with a representative dataset of 110 heart sound signals, taken from healthy and unhealthy medical cases. This system was evaluated in the test dataset with the following performance measurements global accuracy: 98.18%, sensitivity, 96.36% and specificity 100%. This results show the feasibility of classification based on optimized feature extraction and classifier. This paper follows the Association for the recommendations of the Advancement of Medical Instrumentation.

Keywords: congenital heart defects; Heart murmurs; newborns; classification and regression trees;

Amir Mohammad Amiri and Giuliano Armano, “An Intelligent Diagnostic System for Congenital Heart Defects” International Journal of Advanced Computer Science and Applications(IJACSA), 4(7), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040714

@article{Amiri2013,
title = {An Intelligent Diagnostic System for Congenital Heart Defects},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040714},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040714},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {7},
author = {Amir Mohammad Amiri and Giuliano Armano}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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