Ensemble fuzzy multilayer neural perceptron with optimized feature selection for cardiac disease prediction using MRI and ECG data.

One of the major causes of death in the general population is cardiovascular disease. Life-threatening cardiac disease is influenced by several factors, including age, gender, blood sugar, cholesterol, heart rate, and more. There are so many factors that it can be challenging for specialists to assess each one. The current approach utilizes electrocardiogram (ECG) data and magnetic resonance imaging (MRI) image features but suffers from poor performance and high error rates. To address this problem, we employ an ensemble-based fuzzy multilayer neural perceptron (EFMLNP) model to predict cardiac disease. Initially, an image from the University of California, Irvine (UCI) Machine Learning Repository was selected to analyze the prognosis of cardiovascular disease. To effectively replicate the raw data values in the dataset, a median box filter (MBF) is used to pre-process the MRI dataset, reducing irrelevant values. The second stage, segmentation, uses adaptive mean gray segmentation (AMGS) to initialize two clusters for regions of interest and non-interest. The dataset is then tested using a feature-selection method based on recursive spectral spider optimization (RSSO) to identify the most pertinent characteristics for diagnosing heart disease (optimal reduced-feature splitting). Lastly, we examine a machine learning feature-extraction model and perform test analysis on the reduced features. The proposed EFMLNP method is evaluated using metrics including precision, recall, and receiver operating characteristic (ROC). The experimental outcome demonstrates that the accuracy is 98.3%, the precision is 97.15%, the recall is 98.43%, the F1-score is 96.34%, and the ROC is 0.96.