(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Machine learning and adaptation is a collection of machine learning methods consisting of several stacked layers and using data to explore hierarchical abstractions. As computer power has increased and large data has emerged, deep learning is an appropriate structure for cardiological tasks. The need to optimize medical treatment varies from diagnostic to therapeutic in the absence of a medical Centre. Machine learning systems are previous attempts to imitate medical practitioners in their protocol for solving medical tasks or for producing observations. These systems are known not to be useful as they require extensive design features and domain expertise in order to achieve the new cardio data highly accurate and difficult to map. Overall, with any technical progress, cardiometry and medicine are autonomous and become closer to an automated, detailed learning area. But no complete conceptual basis for in-depth education can be found. A thorough analysis of its internal functional qualities and constraints is required to enable the field to adopt its position on the disease of the heart.In this study, a large number of very complex machine learning concepts integrated into the cardio domain with big data have been studied over a very short time period of time.