Automated detection of cardiac arrest in human beings using auto encoders

This research paper investigates the use of Deep learning and Auto-encoders (DAE) for the automated detection of cardiac arrest in human beings. We propose a new method for automated detection that utilizes auto encoders, a type of deep learning model, to detect abnormalities in the cardiovascular system. To achieve this, a dataset of cardiovascular heart disease was used to train and evaluate an auto encoder model. We confirm the efficacy of the proposed approach by evaluating its performance on a dataset of cardiac arrest patients. The results indicate that the proposed technique can accurately detect cardiac arrest in human beings yielding a high degree of accuracy rate of 93%. This study highlights the potential of using DAE for automated detection of cardiac arrest and provides a promising direction for further research in the field. Results from the model show that it is possible to accurately detect cardiac arrest in humans. The performance evaluation metrics are f-measure, accuracy, precision and recall. The proposed DAEalgorithm is compared with other existing approaches are ANN, BPNN, SVM, DT, XG Boost, RF, DNN and SAE.