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

Overview

1. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
2. Kangeyam Institute of Technology, Kangeyam, Tamil Nadu, India

Abstract

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/danialsharifrazi, at Kaggle; found in the text, “Dataset description”
• kaggle.com/datasets/shayanfazeli, at Kaggle; found in the text, “Dataset description”

Data availability statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 8 keywords, 31 references.

Cite

This paper

Kiruthika, J. K., & Thangaraj, P. (2026). Ensemble fuzzy multilayer neural perceptron with optimized feature selection for cardiac disease prediction using MRI and ECG data. Frontiers in physiology, 17, 1719922. https://doi.org/10.3389/fphys.2026.1719922

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