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A diagnostic prediction model of coronary artery disease in patient with chest pain using machine learning.

Session Poster Session 7

Speaker Seung-Woon Rha

Congress : ESC Congress 2019

  • Topic : coronary artery disease, acute coronary syndromes, acute cardiac care
  • Sub-topic : Coronary Artery Disease - Diagnostic Methods
  • Session type : Poster Session
  • FP Number : P6435

Authors : S W Rha (Seoul,KP), B G Choi (Seoul,KP), S Y Choi (Seoul,KP), J K Byun (Seoul,KP), J A Cha (Seoul,KP), TS Park (Richmond,US)

S W Rha1 , B G Choi1 , S Y Choi1 , J K Byun1 , J A Cha1 , TS Park2 , 1Korea University Guro Hospital - Seoul - Korea (Democratic People's Republic of) , 2Virginia Commonwealth University, Division of Cardiology - Richmond - United States of America ,


Background: Chest pain is a major symptom of coronary artery disease (CAD), which can lead to acute coronary syndrome and sudden cardiac death.  Accurate diagnosis of CAD in patients who experience chest pain is crucial to provide appropriate treatment and optimize clinical outcomes.

Objective: This study was to develop a machine learning model which can predict and diagnose CAD in patients complaining of chest pain based on a large real-world prospective registry database and computing power.

Method: A total of 10,177 subjects with typical or atypical chest pain who underwent a coronary angiography at the cardiovascular center of our University Hospital, South Korea between November 2004 and May 2014 were evaluated in this study. . The generation of the diagnostic prediction model for CAD used the classification application by technical support of MATLAB R2017a . The performance evaluation of the learning model generated by machine learning was evaluated by the area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis.

Results: The diagnostic prediction model of CAD had been generated according to the user’s accessibility such as the general public or clinician (Model 1 - 4). The performance of the models has ranged from 0.78 to 0.96 by the AUC of ROC analysis. The prediction accuracy of the models ranged from 70.4% to 88.9%. The performance of the diagnostic prediction model of CAD by machine learning improved as the input information increased.

Conclusion: A diagnostic prediction model of CAD using the machine learning method and the registry database was developed. Further studies are needed to verify our results.

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