A Statistical Study on Intelligent Systems for the Diagnosis of Heart Failure

ML approaches are widely utilized in the domain of health and have a major influence on people's health worldwide. The numerous benefits of such approaches are extensively being used by various health professionals, which lead to a significant enhancement in the healthcare sector. Machine learning approaches are effectively employed in the designing and development of a concurrent health monitoring system based on ML technology. These systems further assist in the prediction of heart failure in its earlier stages so that a patient can treat successfully before his or her condition gets worse. Hence, a number of publications are there that enlightened the implementation and development of such medical diagnostic systems, which assist in the prediction of heart failure by using different approaches of machine learning. Therefore, the main intent of the conducted study is to analyze the different research work presented by various authors on the development of inference systems using ML statistically. This work also investigates other review papers which cover selected approaches and systems along with the research papers which enclose the execution of these models. Different parameters such as the aim of studies, the year in which a particular paper has been published, the evaluated gap in research gap and the final outcome of the paper are used to differentiate the considered papers from one another.