… We aimed to develop a deep learning model (DLM) as a … revolution that started with a deep
learning model (DLM) has … , the performance of the trained model was compared with that of …

… of these models also poses a challenge as clinicians lose confidence in using deep models
in … and compared the performance of two deep learning models for the classification of MI. …

… , deep learning models are being increasingly used in medical fields. In our study, we mainly
evaluated the deep learning model … comparison between the deep learning model and the …

… (67) first try to adopt a deep learning model to predict MS based on ECG and additional
clinical parameters and achieved better performance with an AUC score, sensitivity, specificity, …

… , 2 machine learning models, … a neural network), marginally improved discrimination
compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs …

… By the accuracy test, this study revealed that the accuracy performance of the deep-learning
model was excellent for predicting the prognosis and is better than the conventional risk-…

… In this work, we have presented two novel deep learning models CNN and hybrid CNN-LSTM
along with ensemble techniques for automatic and accurate detection of MI. The data …

… deep models have … models for the diagnosis of myocardial infarction in real-world scenarios.
We aimed to train and validate a deep learning model using ECGs to predict myocardial …

… Myocardial infarction is the leading cause of death worldwide. In this paper, we design
domain-inspired neural network models to detect myocardial infarction. First… myocardial infarction. …

… propose an automatic myocardial infarction segmentation approach on … Neural Networks
(CNN). The objective is to segment … of deep learning models for the fully automatic myocardial …