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XAI–reduct: accuracy preservation despite dimensionality reduction for heart disease classification using explainable AI
Machine learning (ML) has been used for classification of heart diseases for almost a decade, although understanding of the internal working of the black boxes, i.e., non-interpretable models, remain a demanding problem. Another major challenge in such ML models is the curse of dimensionality leadin...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177719/ https://www.ncbi.nlm.nih.gov/pubmed/37359323 http://dx.doi.org/10.1007/s11227-023-05356-3 |