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

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 alg...

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Veröffentlicht in:International journal of intelligent systems Jg. 2025
Hauptverfasser: Aathilakshmi, S, Balasubramaniam, S, Sivakumar, T A, Lakshmi, Chetana V
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York John Wiley & Sons, Inc 01.01.2025
Schlagworte:
Algorithms
Artificial neural networks
Cardiovascular disease
Datasets
Feature selection
Heart attacks
Heart diseases
Medical diagnosis
Methods
Mortality
Neural networks
Optimization
Optimization algorithms
Hungary
ISSN:0884-8173, 1098-111X
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Zusammenfassung: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.
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ISSN:0884-8173
1098-111X
DOI:10.1155/int/1409684