# 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  
This subsection presents the experimental findings obtained by selecting feature subsets using the ACO metaheuristic method. To assess the stability of the ACO feature selection process, we conducted 10 independent runs with the following parameters: 20 ants (solutions) with 30 iterations and a pheromone decay rate of 0.1. The fitness function was set to accuracy to guide the feature selection process. The final feature importance scores were determined by aggregating the results of multiple trials to ensure that the most frequently selected features were prioritized. Table 4 shows the top 10 most essential features selected, ranked by their importance scores. Among them, _HgbA1C_, _dBPBaseline_, and _HistoryDiabetes_ emerged as the most influential features, underscoring their predictive value. The high selection frequency for these features confirms the feature selection process's robustness and stability. Features including _HistorySmoking_, _TimeToEventMonths_, _Gender_, _CreatinineBaseline_, and _HistoryVascular_, recognized as key predictors, continuously scored highly. The most essential features are ranked by their relevance and consistency in selection by including a frequency-based evaluation. It strengthens the stability of the ACO feature selection technique and ensures that the most significant features are prioritized. To provide a comprehensive evaluation, a box plot was generated (see Figure 3), where the x-axis represents independent ACO runs and the y-axis depicts the fitness function (accuracy), illustrating the stability and variance of the algorithm's performance across multiple executions. Additionally, a convergence graph (see Figure 4) was plotted, with the x-axis representing iterations and the y-axis showing the best accuracy obtained over \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Features** & **Score** & **NO.** & **Features** & **Score** \\ \hline
1 & dBPBaseline & 0.0523 & 6 & HistoryDLD & 0.0470 \\
2 & HgbA1C & 0.0489 & 7 & Age.3.categories & 0.0466 \\
3 & HistoryDiabetes & 0.0485 & 8 & TimeToEventMonths & 0.0461 \\
4 & HistoryVascular & 0.0473 & 9 & BMIBaseline & 0.0458 \\
5 & HistoryCHD & 0.0473 & 10 & HistorySmoking & 0.0454 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ACO FS-based selected features—rank and importance score. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifiers** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1-Score** & **Kappa** & **MCC** \\ \hline  & \multicolumn{6}{c}{**Without data preprocessing**} \\ \hline DT & 85.70 & 66.13 & 40.83 & 41.93 & 40.59 & 32.68 & 33.02 \\ RF & 90.39 & 90.02 & 26.67 & 61.67 & 35.71 & 32.50 & 36.55 \\ ET & 90.38 & 88.73 & 21.67 & 65.00 & 31.33 & 28.66 & 34.22 \\ Adaboost & 90.08 & 83.44 & 45.83 & 57.33 & 49.21 & 44.07 & 45.28 \\ XGB & 89.51 & 86.19 & 44.17 & 58.33 & 47.73 & 42.27 & 43.97 \\ LR & 91.51 & 88.49 & 44.17 & 76.67 & 52.00 & 48.04 & 52.12 \\ KNN & 90.08 & 83.17 & 44.17 & 61.67 & 49.76 & 44.58 & 46.25 \\ \hline \hline \multicolumn{6}{c}{**With data preprocessing including the SMOTE data-balancingapproach**} \\ \hline DT & 92.28 & 92.30 & 92.15 & 92.53 & 92.27 & 84.56 & 84.68 \\ RF & 94.58 & 98.64 & 95.42 & 94.04 & 94.59 & 89.15 & 89.40 \\ ET & 95.56 & 99.04 & 96.71 & 94.70 & 95.61 & 91.12 & 91.30 \\ Adaboost & 91.63 & 96.32 & 91.82 & 91.83 & 91.62 & 83.26 & 83.62 \\ XGB & 94.42 & 98.88 & 95.43 & 93.72 & 94.45 & 88.83 & 89.07 \\ LR & 85.38 & 92.44 & 85.28 & 85.63 & 85.26 & 70.77 & 71.08 \\ KNN & 83.90 & 89.87 & 90.49 & 80.26 & 84.85 & 67.79 & 68.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of ML algorithms using full features with and without data preprocessing. successive iterations, demonstrating the steady improvement in solution quality as the ACO progresses. To validate the effectiveness of the ACO, a comparative analysis was conducted with particle swarm optimization (PSO).