Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response

Abstract

Background: In acute cardiovascular disease management, the delay between the admission in a hospital emergency department and the assessment of the disease from a Delayed Enhancement cardiac MRI (DE-MRI) scan is one of the barriers for an immediate management of patients with suspected myocardial infarction or myocarditis.

Objectives: This work targets patients who arrive at the hospital with chest pain and are suspected of having a myocardial infarction or a myocarditis. The main objective is to classify these patients based solely on clinical data in order to provide an early accurate diagnosis.

Methods: Machine learning (ML) and ensemble approaches have been used to construct a framework to automatically classify the patients according to their clinical conditions. 10-fold cross-validation is used during the model's training to avoid overfitting. Approaches such as Stratified, Over-sampling, Under-sampling, NearMiss, and SMOTE were tested in order to address the imbalance of the data (i.e. proportion of cases per pathology). The ground truth is provided by a DE-MRI exam (normal exam, myocarditis or myocardial infarction).

Results: The stacked generalization technique with Over-sampling seems to be the best one providing more than 97% of accuracy corresponding to 11 wrong classifications among 537 cases. Generally speaking, ensemble classifiers such as Stacking provided the best prediction. The five most important features are troponin, age, tobacco, sex and FEVG calculated from echocardiography.

Conclusion: Our study provides a reliable approach to classify the patients in emergency department between myocarditis, myocardial infarction or other patient condition from only clinical information, considering DE-MRI as ground-truth. Among the different machine learning and ensemble techniques tested, the stacked generalization technique is the best one providing an accuracy of 97.4%. This automatic classification could provide a quick answer before imaging exam such as cardiovascular MRI depending on the patient's condition.

Fig 1. Architecture of the proposed approach.

Fig 2. The process of Over-sampling and Under-sampling techniques.

Fig 3. The step by step process of stratified approach with cross validation.

Fig 4. An example of confusion matrix.

0 denotes patients with normal DE-MRI, 1 patients with Myocardial Infarction, and 2 patients with Myocarditis.

Fig 5. Wrapper method for feature importance identification.

Fig 6. Comparison of accuracy of Support Vector Machine (SVM) classifier, K-Nearest Neighbors (KNN), Random Forest (RF), Extremely Randomised Tree (ERT), Gradient Boosting (GB), Decision Tree (DT), Multi-Layer Perceptron (MLP), eXtreme Gradient Boost (XGB), Light Gradient Boost Machine (LGBM) and Stacked generalization (Stacking).

(a) Accuracy distribution of Stratified method (10-fold cross-validation), (b) Accuracy distribution of Stratified and Under-sampling (10-fold cross-validation), (c) Accuracy distribution of Stratified and Over-sampling (10-fold cross-validation), (d) Accuracy distribution of Stratified and NearMiss (10-fold cross-validation, (e) Accuracy distribution of Stratified and SMOTE (10-fold cross-validation).

Fig 7. Comparison of the confusion matrices for the different approaches.

0 denotes patients with normal DE-MRI, 1 patients with Myocardial Infarction, and 2 patients with Myocarditis. (a) Confusion matrix for LGBM (OS). (b) Confusion matrix for LGBM (SMOTE). (c) Confusion matrix for Stacking (OS). (d) Confusion matrix for Stacking (SMOTE).