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Diagnosis of Coronary Arteries Stenosis Using Data Mining

Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important fiel...

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Autores principales: Alizadehsani, Roohallah, Habibi, Jafar, Bahadorian, Behdad, Mashayekhi, Hoda, Ghandeharioun, Asma, Boghrati, Reihane, Sani, Zahra Alizadeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660711/
https://www.ncbi.nlm.nih.gov/pubmed/23717807
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author Alizadehsani, Roohallah
Habibi, Jafar
Bahadorian, Behdad
Mashayekhi, Hoda
Ghandeharioun, Asma
Boghrati, Reihane
Sani, Zahra Alizadeh
author_facet Alizadehsani, Roohallah
Habibi, Jafar
Bahadorian, Behdad
Mashayekhi, Hoda
Ghandeharioun, Asma
Boghrati, Reihane
Sani, Zahra Alizadeh
author_sort Alizadehsani, Roohallah
collection PubMed
description Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task. Moreover, ECG abnormality is not common among CAD patients. The aim of this study is to find a way for specifying the lesioned vessel when there is not enough ECG changes and only based on risk factors, physical examination and Para clinic data. Therefore, a new data set was used which has no missing value and includes new and effective features like Function Class, Dyspnoea, Q Wave, ST Elevation, ST Depression and Tinversion. These data was collected from 303 random visitor of Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Centre, in 2011 fall and 2012 winter. They processed with C4.5, Naïve Bayes, and k-nearest neighbour (KNN) algorithms and their accuracy were measured by tenfold cross validation. In the best method the accuracy of diagnosis of stenosis of each vessel reached to 74.20 ± 5.51% for Left Anterior Descending (LAD), 63.76 ± 9.73% for Left Circumflex and 68.33 ± 6.90% for Right Coronary Artery. The effective features of stenosis of each vessel were found too.
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spelling pubmed-36607112013-05-28 Diagnosis of Coronary Arteries Stenosis Using Data Mining Alizadehsani, Roohallah Habibi, Jafar Bahadorian, Behdad Mashayekhi, Hoda Ghandeharioun, Asma Boghrati, Reihane Sani, Zahra Alizadeh J Med Signals Sens Original Article Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies. Many methods are used to diagnose CAD so far. These methods reduce cost and deaths. But a few studies examined stenosis of each vessel separately. Determination of stenosed coronary artery when significant ECG abnormality exists is not a difficult task. Moreover, ECG abnormality is not common among CAD patients. The aim of this study is to find a way for specifying the lesioned vessel when there is not enough ECG changes and only based on risk factors, physical examination and Para clinic data. Therefore, a new data set was used which has no missing value and includes new and effective features like Function Class, Dyspnoea, Q Wave, ST Elevation, ST Depression and Tinversion. These data was collected from 303 random visitor of Tehran's Shaheed Rajaei Cardiovascular, Medical and Research Centre, in 2011 fall and 2012 winter. They processed with C4.5, Naïve Bayes, and k-nearest neighbour (KNN) algorithms and their accuracy were measured by tenfold cross validation. In the best method the accuracy of diagnosis of stenosis of each vessel reached to 74.20 ± 5.51% for Left Anterior Descending (LAD), 63.76 ± 9.73% for Left Circumflex and 68.33 ± 6.90% for Right Coronary Artery. The effective features of stenosis of each vessel were found too. Medknow Publications & Media Pvt Ltd 2012 /pmc/articles/PMC3660711/ /pubmed/23717807 Text en Copyright: © Journal of Medical Signals and Sensors http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Alizadehsani, Roohallah
Habibi, Jafar
Bahadorian, Behdad
Mashayekhi, Hoda
Ghandeharioun, Asma
Boghrati, Reihane
Sani, Zahra Alizadeh
Diagnosis of Coronary Arteries Stenosis Using Data Mining
title Diagnosis of Coronary Arteries Stenosis Using Data Mining
title_full Diagnosis of Coronary Arteries Stenosis Using Data Mining
title_fullStr Diagnosis of Coronary Arteries Stenosis Using Data Mining
title_full_unstemmed Diagnosis of Coronary Arteries Stenosis Using Data Mining
title_short Diagnosis of Coronary Arteries Stenosis Using Data Mining
title_sort diagnosis of coronary arteries stenosis using data mining
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660711/
https://www.ncbi.nlm.nih.gov/pubmed/23717807
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