Key result
Convolutional neural networks achieved 99% accuracy in predicting arrhythmias and acute myocardial infarctions, compared to 97% for artificial neural networks and 91% for XGBoost.
Why the study?
Cardiovascular diseases like acute myocardial infarction and arrhythmias are leading global health burdens, and evaluating machine learning algorithms on standardized ECG data may assist in their prediction and prevention.
CNN provides the highest accuracy for ECG classification, but XGBoost and ANN offer a better balance of speed and performance for potential real-time u-Health applications.
Answers are grounded in this paper and cite their sources.
May support AI-assisted arrhythmia and MI detection; leaves open prospective clinical validation.
Cardiovascular diseases such as Acute Myocardial Infarction is one of the 3 leading causes of death in the world according to WHO data, in the same way cardiac arrhythmias are very common diseases today, such as atrial fibrillation. The ECG electrocardiogram is the means of cardiac diagnosis that is used in a standardized way throughout the world. Machine learning models are very helpful in classification and prediction problems. Applied to the field of health, ANN, and CNN artificial and neural networks, added to tree-based models such as XGBoost, are of vital help in the prevention and control of heart disease. The present study aims to compare and evaluate learning based on ANN, CNN and XGBoost algorithms by using the Physionet MIT-BIH and PTB ECG databases, which provide ECGs classified with Arrhythmias and Acute Myocardial Infarctions respectively. The learning times and the percentage of Accuracy of the 3 algorithms in the 2 databases are compared separately, and finally the data are crossed to compare the validity and safety of the learning prediction.
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Patiño-Pérez et al. (2023) studied Arrhythmias and Acute Myocardial Infarctions (n=123,994). Convolutional Neural Networks (CNN) vs. Artificial Neural Networks (ANN) and XGBoost was evaluated on Prediction accuracy. Convolutional neural networks achieved 99% accuracy in predicting arrhythmias and acute myocardial infarctions, compared to 97% for artificial neural networks and 91% for XGBoost.
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