ENSEMBLE FUZZY MULTILAYER NEURAL PERCEPTRON WITH OPTIMIZED FEATURE SELECTION FOR CARDIAC DISEASE PREDICTION USING MRI AND ECG DATA

Abstract

One of the biggest causes of death in the general population is cardiovascular disease. Life-threatening cardiac disease is known to be influenced by several factors, including age, gender, blood sugar, cholesterol, heart rate, and more. Still, there are so many that it can be challenging for specialists to assess each one. The approach utilizes ECG and 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 was gathered to analyses the prognosis of cardiovascular disease from the UCI repository. 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 Grey 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 metrics including precision, recall, and Receiver Operating Curve (RoC). The experimental outcome demonstrates the accuracy is 98.3%, precision is 97.15%, recall is 98.43%, F1-score is 96.34% and RoC is 0.96 than other methods.