Neuro-fuzzy modeling, particularly through the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS), offers a promising approach to investigate the complex immunoregulatory role of myeloid-derived suppressor cells (MDSCs) in systemic lupus erythematosus (SLE). Since MDSCs interact with multiple inflammatory pathways and biomarkers, such as IL-10, lactate, and SLE disease activity index (SLEDAI), a neuro-fuzzy system can capture the non-linear, dynamic, and often uncertain relationships between these parameters. By incorporating both granulocytic (G-MDSCs) and monocytic (M-MDSCs) subsets, the model can simulate how distinct MDSC phenotypes contribute to immune tolerance or exacerbation of autoimmunity in SLE, providing insights beyond what traditional statistical models can achieve.

In terms of methodology, MATLAB’s ANFIS toolbox and Python-based libraries such as scikit-fuzzy or TensorFlow-Fuzzy are widely applied for training and testing models with hybrid clinical and experimental data. A key step involves careful data preprocessing—normalizing continuous biomarkers (e.g., IL-10, lactate) while encoding categorical or ordinal clinical indices (e.g., SLEDAI scores, treatment status) in a format compatible with fuzzy membership functions. This integration allows the system to handle heterogeneous datasets typical of SLE research. Moreover, feature selection techniques, such as principal component analysis or correlation-based filtering, can reduce noise and highlight the most influential predictors before model training.

Validation of neuro-fuzzy models in literature-based reviews is more challenging, but still feasible. One approach is to use published datasets, meta-analysis tables, or synthesized evidence from multiple studies to establish input-output relationships. Sensitivity analysis can then be performed to test model robustness against parameter uncertainty. Cross-validation, bootstrapping, or splitting the dataset into training and testing sets can further ensure reliability. Importantly, transparency in defining membership functions, rule extraction, and clinical interpretability of results is crucial for acceptance in biomedical research. Such integration of computational intelligence with immunology could not only clarify the regulatory dynamics of MDSCs in SLE but also open new avenues for predictive modeling in autoimmune disease management.