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Ensemble fuzzy multilayer neural perceptron with optimized feature selection for cardiac disease prediction using MRI
1Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Insights
A new ensemble-based fuzzy multilayer neural perceptron (EFMLNP) model significantly improves cardiovascular disease prediction using MRI data. This advanced machine learning approach achieves high accuracy, offering a more reliable method for diagnosing cardiac conditions.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Cardiovascular Research
Background:
- Cardiovascular disease is a leading cause of mortality globally.
- Accurate prediction of cardiac disease is complex due to numerous influencing factors.
- Current diagnostic methods using ECG and MRI data have limitations in performance and accuracy.
Purpose of the Study:
- To develop and evaluate an advanced machine learning model for improved cardiovascular disease prediction.
- To address the limitations of existing diagnostic approaches for cardiac conditions.
Main Methods:
- Utilized an ensemble-based fuzzy multilayer neural perceptron (EFMLNP) model for cardiac disease prediction.
- Pre-processed MRI data using a median box filter (MBF) and adaptive mean gray segmentation (AMGS).
- Employed recursive spectral spider optimization (RSSO) for feature selection and a machine learning feature-extraction model.
Main Results:
- The EFMLNP model achieved a high accuracy of 98.3%.
- Key performance metrics included precision (97.15%), recall (98.43%), F1-score (96.34%), and ROC (0.96).
- The proposed method demonstrated superior performance in diagnosing cardiac disease compared to existing techniques.
Conclusions:
- The EFMLNP model offers a highly accurate and reliable method for cardiovascular disease prediction.
- This AI-driven approach can aid specialists in assessing cardiac disease risk more effectively.
- The study highlights the potential of advanced machine learning in improving cardiac diagnostics.
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.