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

It is a very difficult task to predict if a patient suffering from heart failure will survive and assist the doctors in making accurate decisions related to the survival of an individual. Proper care of a heart failure patient can only be done if the doctor has the necessary experience in the same domain. Additionally, the numerous linked factors to this disease make the diagnosis more challenging. Various limitations in the diagnostic procedure, data having missing values, inaccurate features of the patient and the inability of medical professionals to determine effects and causes related to the disease all contribute to uncertainty. Hence, to remove this uncertainty in data and help the doctors in the prediction of heart failure, several authors presented and introduced new models. These models use machine learning algorithms which assist in the accurate diagnosis of the disease. Therefore, the main aim of this conducted work is to review these proposed systems, which use machine learning for the diagnosis of heart failure. The considered models are compared on the basis of their classification accuracy. The model which has the highest accuracy of classification is rewarded as a superior system to diagnose heart failure.