# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 5 Experimental Results
### Performance of ML Algorithms Using ACO-Selected Subset of Features
The results indicate that PSO selected 15 features, and an RF classifier using these features achieved an accuracy of 87.84%. In contrast, ACO selected the same number of features; however, RF using ACO-selected features achieved a significantly higher accuracy of 95.95%, highlighting the superiority of ACO in feature selection for this dataset. Additionally, a statistical significance test (T-test) was conducted to compare the accuracy distributions of ACO and PSO. The computed \(p\)-value was 0, confirming that the observed improvement in accuracy using ACO is statistically significant (\(p<0.05\)) and not due to random chance. The selection of ACO's initialization parameters (number of ants, iterations, pheromone decay) was primarily based on insights from theoretical literature studies and previous studies in feature selection. These parameter settings were found to yield optimal performance in our problem context. Furthermore, one of the key challenges of ACO is its  
Figure 4: ACO convergence graph between best accuracy and iterations for 10 runs. Figure 3: Box plot of ACO accuracy across independent runs. computational complexity, particularly in convergence time, which is O(m \(\times\) n \(\times\) I), where m is the number of ants, n is the number of features, and I is the number of iterations. Additionally, ACO is known to be sensitive to parameters such as pheromone decay, which can influence the balance between exploration and exploitation. These aspects should be carefully fine-tuned in practical applications to achieve an optimal trade-off between accuracy and computational efficiency. Table 5 shows the experimental results employing feature subsets selected with the ACO method. Multiple ML classifiers were applied to the top 10 features that significantly impacted KD outcome prediction. The ET classifier outperformed the other models, with an accuracy of 97.70%, precision of 97.15%, recall of 98.37%, F1-score of 97.73%, and an AUC-ROC of 99.55% (marked as bold in Table 5). The accuracy of the other models varied, with DT achieving 89.50%, RF 95.73%, Adaboost 91.47%, XGB 95.08%, LR 89.50%, and KNN 89.66%. ET outperformed the other models, showing an improvement of 6.19% over processed complete feature sets (using an RF classifier with 91.51% accuracy). RF and XGB also demonstrated competitive and balanced performances, highlighting the effectiveness of the selected features in improving KD prediction accuracy. The ET classifier was the most effective model, with an accuracy of 97.70%, as shown in Table 5. Its architecture uses sequential tree development, in which every subsequent tree boosts efficiency by correcting the errors of its predecessors. ET achieved the highest accuracy by using the optimal combinations of hyperparameters as shown in Table 6. Furthermore, its performance was thoroughly investigated and analyzed using the confusion matrix and the ROC curve, which provided more insight into its classification competencies, as seen in Figures 5 and 6. The RF and XGB models performed competitively, with AUC values of 99.17% and 99.03%, respectively. The ROC curves for these models are also shown in Figure 7 for comparison. Although their accuracies (RF: 95.95%;4 XGB: 95.08%) were lower than that for ET, the high AUC scores show that they have a significant discriminatory potential. Based on the ROC curve comparisons, it is evident that ET, RF, and XGB all show outstanding classification skills, with ET attaining the highest overall performance. The ROC curves help choose the best classifier for a situation by evaluating model efficacy more thoroughly. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline bootstrap & False & min\_samples\_leaf & 1 \\ min\_samples\_split & 2 & random\_state & 123 \\ max\_features & sqrt & n\_estimators & 100 \\ criterion & Gini & n\_jobs & \(-1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ET classifier parameters with their optimal values.