Machine learning algorithm can provide assistance for the diagnosis of non-ST-segment elevation myocardial infarction

Abstract

IntroductionOur aim was to use the constructed machine learning (ML) models as auxiliary diagnostic tools to improve the diagnostic accuracy of non-ST-elevation myocardial infarction (NSTEMI).Materials and methodsA total of 2878 patients were included in this retrospective study, including 1409 patients with NSTEMI and 1469 patients with unstable angina pectoris. The clinical and biochemical characteristics of the patients were used to construct the initial attribute set. SelectKBest algorithm was used to determine the most important features. A feature engineering method was applied to create new features correlated strongly to train ML models and obtain promising results. Based on the experimental dataset, the ML models of extreme gradient boosting, support vector machine, random forest, naïve Bayesian, gradient boosting machines and logistic regression were constructed. Each model was verified by test set data, and the diagnostic performance of each model was comprehensively evaluated.ResultsThe six ML models based on the training set all play an auxiliary role in the diagnosis of NSTEMI. Although all models taken for comparison performed differences, the extreme gradient boosting ML model performed the best in terms of accuracy rate (0.95±0.014), precision rate (0.94±0.011), recall rate (0.98±0.003) and F-1 score (0.96±0.007) in NSTEMI.ConclusionsThe ML model constructed based on clinical data can be used as an auxiliary tool to improve the accuracy of NSTEMI diagnosis. According to our comprehensive evaluation, the performance of the extreme gradient boosting model was the best.

A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease

“…The application of ML prediction models to cardiovascular disease has been evaluated previously in patients with ACS [ 42 ]. ML algorithms for CAD have been applied in some clinical settings, including (i) the prediction of CAD using clinical variables and an interdisciplinary approach; (ii) improving the detection of functional CAD using computational hemodynamics (e.g., FFR-based algorithms); and (iii) assessing the ability to automatically predict CAD based on myocardial perfusion imaging.…”