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Explainable Transfer Learning with Residual Attention BiLSTM for Prognosis of Ischemic Heart Disease.
0
Zitationen
6
Autoren
2025
Jahr
Abstract
X-TLRABiLSTM offers a highly accurate, interpretable, and demographically fair framework for IHD prognosis. By combining transfer learning, residual attention, explainable AI, and fairness-aware optimization, this model advances trustworthy AI in healthcare. Its successful performance on benchmark clinical data supports its potential for real-world integration in ethical, AI-assisted cardiovascular diagnostics.
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