Heart Failure diagnosis based on deep learning techniques - Papadopoulos, Theofilos G.; Plati, Daphni; Tripoliti, Evanthia E.; Goletsis, Yorgos; Naka, Katerina K.; Rammos, Aidonis; Bechlioulis, Aris; Watson, Chris; McDonald, Kenneth; Ledwidge, Mark; Pharithi, Rebabonye; Gallagher, Joseph; et al. (2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021)
The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs) are used to pre-train Deep Belief Networks (DBN) and Deep Neural Networks (DNN). The data is provided by the University College Dublin and the 2nd Department of Cardiology from the University Hospital of Ioannina. The features recorded are grouped into: general demographic information, physical examination, classical cardiovascular risk factors, personal history of cardiovascular disease, symptoms, medications, echocardiographic features, laboratory findings, lifestyle/habits and other diseases. The total number of subjects utilized is 422. The deep learning methods provide quite high results with the Autoencoder plus DNN approach to demonstrate accuracy 91.71%, sensitivity 90.74%, specificity 92.31% and f-score 89.36%.