Nag, A.Das, B.Sil, R.Hameed, A.A.Bhushan, B.Jamil, A.2024-05-192024-05-19202497830315371651860-949Xhttps://doi.org/10.1007/978-3-031-53717-2_44https://hdl.handle.net/20.500.12713/42702nd International Conference on Computing, IoT and Data Analytics, ICCIDA 2023 -- 20 July 2023 through 21 July 2023 -- -- 308639Cardiac imaging is crucial in the diagnosis of cardiovascular disease. Cardiovascular disease is the umbrella term for the majority of heart ailments. The majority of the causes of mortality are associated with cardiovascular illness. The authors provide a technique for the diagnosis of cardiac disease. The main aim of this study is to determine the most effective technique for predicting cardiovascular disease, specifically focusing on the use of signs of heart disease and Electrocardiogram images. This will be achieved by leveraging the latest advancements in Deep Learning and Machine Learning methods. The authors conduct a comprehensive examination of various Machine Learning and Deep Learning Techniques. These techniques were evaluated in the context of predicting cardiovascular disease evaluating Image. The analysis shows that the Convolutional Neural Network methods are much more effective than the alternatives. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.eninfo:eu-repo/semantics/closedAccessCardiac İmagingCardiovascular DiseaseCnnDeep LearningEcg İmagesMachine LearningConference Object1145 SCI4784912-s2.0-8518799631210.1007/978-3-031-53717-2_44N/A