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A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization

  • Patient Facing Systems
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Abstract

The aim of this study is to predict acute coronary syndrome (ACS) requiring revascularization in those patients presenting early-stage angina-like symptom using machine learning algorithms. We obtained data from 2344 ACS patients, who required revascularization and from 3538 non-ACS patients. We analyzed 20 features that are relevant to ACS using standard algorithms, support vector machines and linear discriminant analysis. Based on feature pattern and filter characteristics, we analyzed and extracted a strong prediction function out of the 20 selected features. The obtained prediction functions are relevant showing the area under curve of 0.860 for the prediction of ACS that requiring revascularization. Some features are missing in many data though they are considered to be very informative; it turned out that omitting those features from the input and using more data without those features for training improves the prediction accuracy. Additionally, from the investigation using the receiver operating characteristic curves, a reliable prediction of 2.60% of non-ACS patients could be made with a specificity of 1.0. For those 2.60% non-ACS patients, we can consider the recommendation of medical treatment without risking misdiagnosis of the patients requiring revascularization. We investigated prediction algorithm to select ACS patients requiring revascularization and non-ACS patients presenting angina-like symptoms at an early stage. In the future, a large cohort study is necessary to increase the prediction accuracy and confirm the possibility of safely discriminating the non-ACS patients from the ACS patients with confidence.

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Acknowledgments

This research was supported by EMBRI Grants 2017SN0002 from the Eulji University and partly supported by the National Research Foundation of Korea Grant NRF/MSIT-2017R1E1A1A03070945 funded by the Korea government.

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Author notes

  1. Yung-Kyun Noh and Ji Young Park contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea

    Yung-Kyun Noh

  2. Division of Cardiology, Nowon Eulji Hospital, Eulji University, 68 Hangeulbiseok-ro, Nowon-gu, Seoul, 01830, South Korea

    Ji Young Park

  3. Cardiovascular Center, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea

    Byoung Geol Choi & Seung-Woon Rha

  4. School of Computing, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea

    Kee-Eung Kim

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  1. Yung-Kyun Noh
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  2. Ji Young Park
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Correspondence to Seung-Woon Rha.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Noh, YK., Park, J.Y., Choi, B.G. et al. A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. J Med Syst 43, 253 (2019). https://doi.org/10.1007/s10916-019-1359-5

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