Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves’ Features

Treadmill Exercise Test (TET) results and patients' clinical symptoms influence cardiologists' decision to perform Coronary Angiography (CAG) which is an invasive procedure. Since TET has high false positive rates, it can cause an unnecessary invasive CAG. Our primary objective was to deve...

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Bibliographic Details
Published in:Current problems in cardiology 2023-02, Vol.48 (2), p.101482-101482, Article 101482
Main Authors: Yilmaz, Abdurrahim, Hayıroğlu, Mert İlker, Salturk, Serkan, Pay, Levent, Demircali, Ali Anil, Coşkun, Cahit, Varol, Rahmetullah, Tezen, Ozan, Eren, Semih, Çetin, Tuğba, Tekkeşin, Ahmet İlker, Uvet, Huseyin
Format: Article
Language:English
Subjects:
Coronary Angiography
Coronary Artery Disease - diagnosis
Electrocardiography
Exercise Test - methods
Humans
Machine Learning
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Summary:Treadmill Exercise Test (TET) results and patients' clinical symptoms influence cardiologists' decision to perform Coronary Angiography (CAG) which is an invasive procedure. Since TET has high false positive rates, it can cause an unnecessary invasive CAG. Our primary objective was to develop a machine learning model capable of optimizing TET performance based on electrocardiography (ECG) waves characteristics and signals. TET reports from 294 patients who underwent CAG following high risk TET were collected and categorized into those with critical CAD and others. The signal was converted to time series format. A dataset containing the P, QRS, and T wave times and amplitudes was created. Using this dataset, 5 machine learning algorithms were trained with 5-fold cross validation. All these models were then compared to the performance of cardiologists on V5 signal. The results from 5 machine learning models were clearly superior to the cardiologists' V5 signal performance (P < 0.0001). In addition, the XGBoost model, with an accuracy of 80.92±6.42% and an area under the curve (AUC) of 0.78±0.06, was the most successful model. Machine learning models can produce high-performance diagnoses using the V5 signal markers only as it does not require any clinical markers obtained from TET reports. This can lead to significant contributions to improving clinical prediction in non-invasive methods.
ISSN:0146-2806
1535-6280
DOI:10.1016/j.cpcardiol.2022.101482