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Min–Max Filtering and Exponential Fossa Optimization Algorithm–Based Parallel Convolutional Neural Network for Heart Disease Detection.

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Název: Min–Max Filtering and Exponential Fossa Optimization Algorithm–Based Parallel Convolutional Neural Network for Heart Disease Detection.
Autoři: S., Aathilakshmi, S., Balasubramaniam, T. A., Sivakumar, V., Lakshmi Chetana, Vocaturo, Eugenio
Zdroj: International Journal of Intelligent Systems; 2/13/2025, Vol. 2025, p1-13, 13p
Témata: CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, FEATURE selection, HEART diseases, MOVING average process
Abstrakt: Heart disease is a leading cause of death worldwide, affecting millions of lives each year. Earlier and more accurate heart disease detection helps people to save their valuable lives. Many existing systems remain costly and inaccurate. To overcome these issues, an exponential fossa optimization algorithm–based parallel convolutional neural network (EFOA‐PCNN) is proposed in this paper for efficient heart disease detection. Initially, the heart disease data are allowed for data normalization, which is performed by min–max normalization. These normalized data are forwarded to the feature selection phase, which is conducted based on chord distance. Finally, heart disease detection is performed using a parallel convolutional neural network (PCNN) that is trained using the EFOA. Here, the EFOA is developed by the combination of the fossa optimization algorithm (FOA) and exponentially weighted moving average (EWMA). The performance of the proposed EFOA‐PCNN is analysed by three metrics, such as specificity, sensitivity, and accuracy, and the F1 score that gained superior values of 91.95%, 91.76%, 91.86%, and 92.39%. These results highlight the robustness and reliability of the proposed method in comparison to traditional approaches. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Heart disease is a leading cause of death worldwide, affecting millions of lives each year. Earlier and more accurate heart disease detection helps people to save their valuable lives. Many existing systems remain costly and inaccurate. To overcome these issues, an exponential fossa optimization algorithm–based parallel convolutional neural network (EFOA‐PCNN) is proposed in this paper for efficient heart disease detection. Initially, the heart disease data are allowed for data normalization, which is performed by min–max normalization. These normalized data are forwarded to the feature selection phase, which is conducted based on chord distance. Finally, heart disease detection is performed using a parallel convolutional neural network (PCNN) that is trained using the EFOA. Here, the EFOA is developed by the combination of the fossa optimization algorithm (FOA) and exponentially weighted moving average (EWMA). The performance of the proposed EFOA‐PCNN is analysed by three metrics, such as specificity, sensitivity, and accuracy, and the F1 score that gained superior values of 91.95%, 91.76%, 91.86%, and 92.39%. These results highlight the robustness and reliability of the proposed method in comparison to traditional approaches. [ABSTRACT FROM AUTHOR]
ISSN:08848173
DOI:10.1155/int/1409684