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This document outlines a study that introduces a gene expression-based diagnostic framework for early detection of Ankylosing Spondylitis (AS), addressing limitations of traditional imaging methods. It describes a novel hybrid machine learning model that combines Multi-Layer Perceptron and Random Forest classifiers, enhancing diagnostic performance and interpretability. The model achieves high predictive accuracy and provides insights into genetic indicators for AS, improving clinical interpretability.
- Author
- gss_1987
- Language
- EN