Advanced Heart Disease Prediction Through Spatial and Temporal Feature Learning with SCN-Deep BiLSTM
Abstract Heart disease prediction using machine learning methods faces various challenges, such as low data quality, missing irrelevant values, and underfit and overfit problems, which increase the time complexity and degrade the model's prediction performance. Moreover, the hybrid models for h...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00734-6 |
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