Why the study?
Heart failure is often diagnosed late because screenings are complex and costly, creating a need for novel early detection methods.
Does a Fuzzy Inference System improve the accuracy of early heart disease detection compared to an Artificial Neural Network?
Does a Fuzzy Inference System improve the accuracy of early heart disease detection compared to an Artificial Neural Network?
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An artificial intelligence model using a Fuzzy Inference System demonstrated high accuracy (90.50%) for early detection of heart failure risk, outperforming a standard neural network approach.
Heart failure, a chronic and progressive condition where the heart muscle fails to pump sufficient blood for the body’s needs, leads to complications like irregular heartbeat and organ damage. It is a leading cause of death worldwide, with 17.9 million annual fatalities. Often diagnosed late due to complex, costly screenings, current treatments are less effective at advanced stages, necessitating novel early detection methods. This research develops intelligent systems using a Fuzzy Inference System and Feed Forward Back Propagation Neural Network, focusing on eleven heart-affecting parameters. The study shows artificial intelligence-based models outperform current medical diagnostics in early heart disease detection. The models were evaluated using 221 datasets. 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 interface (GUI) is developed using the MATLAB App Designer tool to facilitate the system’s practical applicability for the end users.
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Rabbi et al. (2024) studied this question.
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