An intelligent system approach for predicting the risk of heart failure
Heart failure is a chronic, progressive condition in which the heart muscle is unable to pump enough blood to meet the body’s needs for blood and oxygen. It is a severe and long-term condition and there are several complications from heart failure that include irregular heartbeat, sudden cardiac arrest, heart valve problems, pulmonary hypertension, kidney damage, liver damage, malnutrition etc. According to the World Health Organization (WHO), the number one cause of death in cardiovascular diseases (CVD) is estimated at 17.9 million a year, which accounts for 31% of all deaths worldwide. The majority of heart patients are diagnosed at a stage of high risk since the early screening and diagnosis of any heart disease is complicated and the particular medical exams are expensive. The current therapeutic approaches lose their effectiveness at this time, which can have deadly repercussions. To lower the mortality rate, novel methods for the early identification of cardiac disease are therefore vital. The research aims to create intelligent systems that can help doctors identify heart disease more quickly and affordably. The likelihood of a patient's survival will rise with the early discovery of potential damage in the system of the heart. The thesis provides a Fuzzy Inference System approach and Feed Forward Back Propagation Neural Network approach to develop intelligent systems based on some input parameters. There are so many factors that can affect the system of the heart. This research uses eleven major parameters to predict the risk of heart failure. The primary outcome of this study is that modelling based on artificial intelligence approaches is far more successful than what is currently available in the medical field for the early detection of heart disease. The performance of the developed systems has been evaluated by a confusion matrix based on 221 datasets collected from a valid source. The obtained result demonstrates that the performance parameters of the FIS model provide superior results compared to the ANN model. The developed FIS system's accuracy, precision, sensitivity, and specificity are 90.50%, 90.91%, 90.50% and 90.31%, respectively. A Graphical User Interference (GUI) is developed using the MATLAB App designer tool to facilitate the system’s practical applicability for the end-users.
Networked Digital Library of Theses and Dissertations
Faculty of Graduate Studies and Research, University of Regina