# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 4 Methodology
### Classification
#### 4.2.2 MLSpecialReduct
* **Statistical testing**:
* Paired \(t\)-tests (\(\alpha=0.01\)) on fold-wise performance metrics;
* Bonferroni correction for multiple comparisons;
* Effect sizes reported via Cohen's d.

All results are reported as the **mean \(\pm\) standard deviation** across 10 folds. ### Model Evaluation

#### 5.3.1 Model Evaluation Without Rough Set Theory Feature Selection

In this section, we evaluate our model's performance without applying Rough Set Theory (RST) for feature selection. This comparison is important as it highlights the substantial advantages RST provides. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Step** & **Technique** & **Rationale** \\ \hline Discretization & Clinical binning & \(\bullet\) & Age: \(<\)40/40–60/\(>\)60 (WHO categories) \\  & & \(\bullet\) & BP: JNC7 hypertension stages \\  & & \(\bullet\) & Cholesterol: NCEP ATP III guidelines \\ \hline Normalization & Min-Max [0, 1] & \(\bullet\) & Preserves original distributions \\  & & \(\bullet\) & Compatible with RST rough approximations \\ \hline Encoding & Ordinal/One-hot & \(\bullet\) & Ordinal for graded features (e.g., chest pain) \\  & & \(\bullet\) & One-hot for nominal (e.g., thalassemia) \\ \hline Outliers & IQR (1.5\(\times\)) & \(\bullet\) & Removed 27 extreme lab values \\  & & \(\bullet\) & Verified as measurement errors \\ \hline Class Balance & SMOTE & \(\bullet\) & Corrected 45/55 imbalance in UCI data \\  & & \(\bullet\) & Applied to both datasets uniformly \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Preprocessing steps with clinical rationale. To establish a baseline, we first used traditional feature selection methods to train our models. These included common techniques like Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and basic statistical methods. While these approaches are popular and have their strengths, they often fail to fully capture the complex patterns and relationships present in the data. In Table 7 below, we compare the performance of several machine learning models without applying RST feature selection. The key metrics for comparison are precision, recall, and F1-score. Among all classifier models, we found that the best models with this first model evaluation (without feature selection) were Gaussian Process (Figure 2) and AdaBoost (Figure 3). #### 5.3.2 Evaluation with MLRduct Feature Selection

Feature selection using **MLReduct** excels in high-dimensional datasets, where redundant or irrelevant attributes can mask patterns and hinder model efficiency. By applying RST to reduce dimensionality, MLRduct retains critical predictive features, simplifying models and boosting computational performance. Benefits include the following:

* **Model Simplification:** Reducing the number of features simplifies the machine learning model, which enhances its interpretability. Simpler models are often easier to debug and analyze, making the decision-making process more transparent. * **Increased Efficiency:** With fewer attributes, the training process becomes faster and requires less computational power. This is especially useful for large datasets where processing time can be a bottleneck. * **Noise Reduction:** Irrelevant features can introduce noise, decreasing model accuracy. By eliminating unnecessary attributes, the MLRduct method improves the quality of the model, leading to better generalization on unseen data. * **Improved Model Performance:** Feature selection via the MLRduct method often results in improved predictive performance. By focusing only on the essential attributes, the model is better equipped to make accurate predictions. * **Baseline for Comparison:** Comparing models with all attributes versus MLRduct-selected features validates its impact on optimization. In Table 8 below, we compare the performance of several machine learning models both before and after applying the MLRduct method. The key metrics for comparison are precision, recall, and F1-score.