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

Yung-Kyun Noh, Ji Young Park, Byoung Geol Choi, Kee Eung Kim, Seung Woon Rha

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Article number253
JournalJournal of Medical Systems
Volume43
Issue number8
DOIs
StatePublished - 2019 Aug 1

Fingerprint

Acute Coronary Syndrome
Learning systems
Discriminant analysis
Machine Learning
Learning algorithms
Support vector machines
Discriminant Analysis
Diagnostic Errors
ROC Curve
Area Under Curve
Cohort Studies

Keywords

  • Acute coronary syndrome
  • Diagnosis
  • Machine learning

Cite this

Noh, Yung-Kyun ; Park, Ji Young ; Choi, Byoung Geol ; Kim, Kee Eung ; Rha, Seung Woon. / A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. In: Journal of Medical Systems. 2019 ; Vol. 43, No. 8.
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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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A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. / Noh, Yung-Kyun; Park, Ji Young; Choi, Byoung Geol; Kim, Kee Eung; Rha, Seung Woon.

In: Journal of Medical Systems, Vol. 43, No. 8, 253, 01.08.2019.

Research output: Contribution to journalArticleResearchpeer-review

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