# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 4 Methodology
### Classification
#### 4.2.2 MLSpecialReduct
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** \\ \hline XGBoost [13] & 0.90 & 0.90 & 0.90 \\ \hline LightGBM [43] & 0.91 & 0.91 & 0.91 \\ \hline Random Forest ( MLFuzzyRoughSet ) & 0.83 & 0.83 & 0.83 \\ \hline Random Forest ( MLReduct ) & 0.87 & 0.87 & 0.87 \\ \hline Random Forest ( MLSpecialReduct ) & 0.99 & 0.99 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison of classifiers using RST-based feature selection with state-of-the-art classifiers. * **MLVarianceThreshold:** Yielded accuracies of 0.72-0.77 across classifiers, underscoring the limitations of traditional variance-based selection compared to RST approaches. Despite achieving 99% accuracy, we mitigated overfitting through the following:
* Stratified 10-fold cross-validation;
* L2 regularization in all classifiers (e.g., SVM, Neural Net);
* Hold-out validation (20% unseen data). The consistency of results across folds (std. dev. \(\leq\) 0.03) further supports model robustness. These results highlight the transformative potential of RST-based methods, enhancing accuracy, efficiency, and interpretability in machine learning, especially for imperfect or uncertain data. ### Practical Implications and Future Directions

Our findings offer practical benefits for optimizing machine learning in fields like healthcare diagnostics and IoT systems. Methods like MLSpecialReduct and MLReduct distill minimal, discriminative feature sets, reducing computational load while preserving performance. Their inherent interpretability makes them ideal for explainable AI (XAI), fostering trust in high-stakes applications. Future research should explore integrating RST with paradigms like deep learning, ensemble methods, or reinforcement learning to enhance performance further. Applying these techniques to diverse, real-world datasets--featuring imbalance, noise, or high dimensionality--will test their adaptability. Investigating their use in dynamic contexts, such as real-time or online learning, could enable adaptive models. Developing automated, scalable RST-based frameworks and evaluating their impact on interpretability and efficiency will drive progress toward advanced, transparent AI systems. Conceptualization, S.N. and O.E.O. ; Methodology, O.E.O. ; Software, O.E.O. ; Writing--review & editing, S.N. All authors have read and agreed to the published version of the manuscript. This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. KFU251576]. Not applicable. The data presented in this study are available on request from the corresponding author due to privacy restrictions, as it's a private cardiovascular dataset collected from partner hospitals. This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. KFU251576]. The authors declare no conflicts of interest. ## References

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