Resumen

PATINO, Darwin et al. Improvement of the lactose grinding process for a company manufacturing dairy products. Ingenius [online]. 2023, n.29, pp.79-89. ISSN 1390-860X.  https://doi.org/10.17163/ings.n29.2023.07.

Cardiovascular diseases such as Acute Myocardial Infarction are one of the 3 leading causes of death worldwide, according to WHO data. Similarly, cardiac arrhythmias, such as atrial fibrillation, are very common diseases at present. The electrocardiogram (ECG) is the means of cardiac diagnosis that is used in a standardized way worldwide. Machine learning models are very helpful in classification and prediction problems. Applied to the field of health, artificial neural networks (ANN) and convolutional neural networks (CNN) together with tree-based models such as XGBoost, are of vital help in the prevention and control of heart diseases. The objective of the present study is to compare and evaluate the learning based on the ANN, CNN and XGBoost algorithms 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 on the 2 databases are compared separately, and finally the data are crossed to compare the validity and safety of the prediction.

Palabras clave : arrhythmias; acute myocardial infarction; machine learning; artificial neural network; convolutional neural network; extreme gradient boosting.

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