The early and accurate detection of the onset of acute myocardial infarction (AMI) is imperative for the timely provision of medical intervention and the reduction of its mortality rate. Machine learning techniques have demonstrated great potential in aiding disease diagnosis. In this paper, we present a framework to predict the onset of AMI using 713,447 extracted ECG samples and associated auxiliary data from the longitudinal and comprehensive ECG-ViEW II database, previously unexplored in the field of machine learning in healthcare. The framework is realized with two deep learning models, a convolutional neural network (CNN) and a recurrent neural network (RNN), and a decision-tree based model, XGBoost. Synthetic minority oversampling technique (SMOTE) was utilized to address class imbalance. High prediction accuracy of 89.9%, 84.6%, 97.5% and ROC curve areas of 90.7%, 82.9%, 96.5% have been achieved for the best CNN, RNN, and XGBoost models, respectively. Shapley values were utilized to identify the features that contributed most to the classification decision with XGBoost, demonstrating the high impact of auxiliary inputs such as age and sex. This paper demonstrates the promising application of explainable machine learning in the field of cardiovascular disease prediction. INDEX TERMS Machine learning, biomedical informatics, predictive models, acute myocardial infarction.
β-VAE is a follow-up technique to variational autoencoders that proposes special weighting of the KL divergence term in the VAE loss to obtain disentangled representations. Unsupervised learning is known to be brittle even on toy datasets and a meaningful, mathematically precise definition of disentanglement remains difficult to find. Here we investigate the original β-VAE paper and add evidence to the results previously obtained indicating its lack of reproducibility. We also further expand the experimentation of the models and include further more complex datasets in the analysis. We also implement an FID scoring metric for the β-VAE model and conclude a qualitative analysis of the results obtained. We end with a brief discussion on possible future investigations that can be conducted to add more robustness to the claims.
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