Recursive Statistical Invariants Elimination for High-Risk Disease Diagnosis with Feature Selection

Feature selection for high-risk disease diagnosis is critical for accurate risk assessment, yet existing methods fail to incorporate medical domain knowledge, potentially leading to suboptimal diagnostic decisions. To address this limitation, we propose recursive statistical invariants elimination (RSIE), a novel framework that systematically integrates medical domain knowledge into feature selection. RSIE comprises three complementary components: learning using statistical invariants (LUSI) for extracting reliable diagnostic patterns from domain knowledge, kernel alignment for establishing meaningful correlations between domain knowledge and dataset features, and recursive feature elimination (RFE) for systematic feature ranking and selection. We evaluate RSIE on 15 high-risk disease datasets spanning cardiovascular, neurological, and oncological conditions. Experimental results show that RSIE achieves an average accuracy of 88.74% across all datasets compared to 83.28% for traditional SVM-RFE, representing a 5.46% improvement. Specifically, the LUSI-RFE* variant achieves superior performance, reducing feature dimensions by 45%-74% while maintaining high classification accuracy. The experiments simultaneously demonstrate the stability of RSIE against high noise conditions, computational efficiency, and model interpretability for high-risk disease diagnosis.