Hybrid Whale Optimisation with Improved Pooling Based Recurrent Neural Network to Predict the Heart Murmur at Earlier Stage

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 5

Year of Publication : 2024

Authors : M. Hemalatha, Zayaraz Godandapani

10.14445/23488549/IJECE-V11I5P102

How to Cite?

M. Hemalatha, Zayaraz Godandapani, "Hybrid Whale Optimisation with Improved Pooling Based Recurrent Neural Network to Predict the Heart Murmur at Earlier Stage," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 5, pp. 18-26, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I5P102

Abstract:

Heart murmurs are abnormal sounds produced during cardiac cycles that can indicate underlying cardiovascular disorders. Early detection of heart murmurs is crucial for timely intervention and enhanced patient results. This paper proposed a hybrid method for early stage heart murmur prediction using a combination of the Whale Optimization Algorithm (WOA) and an Improved pooling-based Recurrent Neural Network (IRNN). The improved pooling mechanism enhances the RNN's ability to capture relevant features from the input data while reducing the computational complexity. Our proposed Model (WOA-IRNN) is able to learn the long-term reliance on the heart sound signals and identify the subtle patterns that are indicative of heart murmur. Experimental results demonstrate that the Whale Optimization with improved pooling-based RNN outperforms existing methods. We evaluated the WOA-IRNN method on a dataset of heart sound signals and realised a high accuracy of 98.9% in predicting heart murmur. The consequences illustrate that our system significantly progresses the prediction accuracy, demonstrating its potential for early stage heart murmur detection.

Keywords:

Heart murmurs, Recurrent Neural Network, Whale Optimization Algorithm, Early detection, and classification.

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