Key result
A simple multilayer perceptron (MLP) model for automated cardiovascular disease prediction using ECG signals achieved a 94.40% accuracy score, outperforming existing approaches.
Why the study?
Manual ECG analysis by experts is time-consuming and error-prone, while automated identification using state-of-the-art complex deep learning architectures carries high computational complexity.
Does a multilayer perceptron (MLP) model improve the accuracy of automated cardiovascular disease prediction from ECG signals compared to existing deep learning and machine learning models?
Does a multilayer perceptron (MLP) model improve the accuracy of automated cardiovascular disease prediction from ECG signals compared to existing deep learning and machine learning models?
A simple multilayer perceptron model can achieve high accuracy (94.40%) in automated cardiovascular disease prediction from ECG signals, potentially reducing computational complexity compared to complex deep learning architectures.
May support automated ECG analysis for cardiovascular disease; leaves open need for prospective validation before clinical adoption.
Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals with window size 10 is used as an input. Several competing deep learning and machine learning models are used for comparison. K-fold cross-validation is used to validate the results. Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40% accuracy score. The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world, practical medical environment.
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Tayyeb et al. (2023) studied Cardiovascular disease. Multilayer perceptron (MLP) model vs. Existing deep learning and machine learning models was evaluated on Accuracy score for heart disease prediction. A simple multilayer perceptron (MLP) model for automated cardiovascular disease prediction using ECG signals achieved a 94.40% accuracy score, outperforming existing approaches.
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