Enhancing heart disease classification with M2MASC and CNN-BiLSTM integration for improved accuracy
Abstract Heart disease is a leading cause of death globally; therefore, accurate detection and classification are prominent, and several DL and ML methods have been developed over the last decade. However, the classical approaches may be prone to overfitting and under fitting issues, and the model p...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-74993-2 |
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