Semi-Supervised Dual-Manifold Regularized Fuzzy Broad Learning for ICU Admission Prediction in Post-COVID Transplant Recipients

This paper introduces a novel semi-supervised Dual-Manifold Regularized Fuzzy Broad Learning System (DMR-FBLS) designed for predicting intensive care unit (ICU) admissions among post-COVID organ transplant recipients. Addressing the challenge of increased mortality risk and strained medical resources among patients who are COVID-19-infected transplant recipients, our study fills the gap by employing machine learning to predict the ICU requirements for this particular group of patients. The DMR-FBLS uniquely integrates two manifold regularizations—feature and sample manifold regularization—into the fuzzy broad learning system (FBLS) framework. This integration enhances the system's capacity to utilize the latent structural relationships within the data. Evaluated using the IDOTCOVID database, the DMR-FBLS demonstrated superior performance over other models in terms of accuracy, F1-score, and G-mean. In scenarios with limited sample availability, DMR-FBLS outperformed both the FBLS and TSK fuzzy systems in terms of accuracy and G-mean. This underscores the efficacy of DMR-FBLS as a semi-supervised learning fuzzy system, accurately predicting ICU demands and thereby providing decision support for medical professionals in optimizing resource allocation for transplant patients.