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Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick
Sheikh Shah Mohammad Motiur Rahman1, Zhihao Chen1, Alain Lalande2,3
1FEMTO-ST Institute, CNRS UMR 6174, Univ. Bourgogne Franche-Comté, Belfort, France.
Insights
This study developed a machine learning model to quickly diagnose myocardial infarction or myocarditis using only clinical data, achieving over 97% accuracy. This approach aids early patient management in emergency departments before imaging.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Delayed Enhancement cardiac MRI (DE-MRI) assessment delays acute cardiovascular disease management.
- Patients with suspected myocardial infarction or myocarditis face diagnostic delays in emergency departments.
Purpose of the Study:
- To classify patients with suspected myocardial infarction or myocarditis using solely clinical data.
- To provide an early and accurate diagnosis, reducing reliance on immediate imaging.
Main Methods:
- Developed a machine learning framework using ensemble approaches for patient classification.
- Employed 10-fold cross-validation and data-balancing techniques (Over-sampling, SMOTE, etc.).
- Utilized DE-MRI results as the ground truth for training and validation.
Main Results:
- The stacked generalization technique with Over-sampling achieved over 97% accuracy.
- Identified key predictive features: troponin, age, tobacco use, sex, and FEVG.
- Ensemble classifiers, particularly Stacking, demonstrated superior prediction capabilities.
Conclusions:
- A reliable method for classifying emergency department patients with suspected cardiac conditions using clinical data was established.
- Stacked generalization achieved 97.4% accuracy, offering a rapid diagnostic alternative.
- This automated classification can expedite patient assessment prior to cardiovascular MRI.
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.
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