Related Factors Mining of Diabetes Complications Based on Manifold-Constrained Multi-Label Feature Selection

The primary cause of mortality among individuals with diabetes stems from complications. Identifying related factors for these complications holds immense potential for early prevention. Previous research predominantly employed traditional machine-learning techniques to establish prediction models utilizing medical indicators for related factor selection. However, uncovering the intricate correlations among complication labels and identifying similar characteristics among medical indicators has been challenging. We propose a novel embedded multi-label feature selection approach called LCFSM(Label Cosine and Feature Similar Manifold) to address the issue. LCFSM introduces manifold constraints into the objective function to uncover risk factors associated with diabetes complications. Label cosine similarity is set to optimize feature weights, forming label manifold constraints. Similarly, feature manifold constraints are established to utilize feature kernel similarity in optimizing feature weights. LCFSM formulates an objective function based on the
$\ell _{2,1}$
regularized Least Squares and previous manifolds constraints, employing the Sylvester equation for convergence assurance. The experimental evaluation compares LCFSM against eight baselines, demonstrating superior performance in top-10 feature selection and feature stacking.LCFSM is applied to identify primary risk factors for diabetes complications. Related factors involve Electromyogram, Urine Routine Protein Positive, etc, offering valuable insights for early treatment.