# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 5 Experimental Results
### Explainable LIME Analysis  
LIME approaches were also used to examine and interpret predictions from the ML models, both locally and globally. Figures 12 and 13 provide random instances for local interpretation, emphasizing features that affected the differentiation of patients into KD-Yes (1, shown in orange) or KD-No (0, shown in blue). The visualizations provide the precise values of these characteristics and information about how they contributed to the prediction. Each figure has three parts: left, center, and right. The anticipated results for each patient in both classes (0 or 1) are shown in the left section. The center part lists the top 10 significant characteristics that drive the classifications, in descending order of importance. Longer bars signify a larger influence on the predicted features, and each bar's  
Figure 12: LIME confidence intervals for patient ID 55 using the ET classifier. Figure 13: LIME confidence intervals for patient ID 63 using the ET classifier. length represents the importance of the relevant feature. The important characteristics of these prominent features at their maximum contribution to the prediction are highlighted in the rightmost area. Figures 12 and 13 show that the ET model has a 94% confidence interval in predicting KD-Yes for Patient ID 55 (Figure 12). Features like _DMmeds_, _HistoryDiabetes_, and _HgbA1C_ significantly impact the prediction toward KD-Yes, while _TimeToEventMonths_ and _HistoryCHD_ endorse KD-No for this patient. Patient ID 63, on the other hand, is 100% confidently identified as KD-No in Figure 13. _TimeToEventMonths_ is the sole factor that marginally supports KD-Yes, but _DMmeds_, _HistoryDiabetes_, _ACEIARB_, and _eGFRBaseline_ are key features that affect this prediction. The interpretability of the model's predictions is improved by these visual charts, which provide insightful information about the contributions of input features. The global feature importance for KD prediction using the ET classifier, as examined using LIME, is shown in Figure 14. The most important factors that greatly assist in predicting KD are _DMmeds_, _HistoryDiabetes_, _DLDmeds_, and _HistoryCHD_. On the other hand, characteristics like _ACEIARB_, _TimeToEventMonths_, and _HistoryHTN_ show the most negative significance, lowering the risk of KD development and reflecting clinical markers of improved kidney health. Features such as _HistoryVascular_ and _HistorySmoking_ have minimal impact, suggesting that they are irrelevant to the dataset. This study emphasizes the interpretability of the model and identifies important factors that affect the results of KD classification. The LIME analysis offers valuable insights into the factors influencing KD prediction. In line with clinical understanding, the most influential features identified in local and global interpretations include _DMmeds_, _HistoryDiabetes_, _HgbA1C_, _eGFRBaseline_, _HistoryCHD_, and _ACEIARB_. For example, _DMmeds_ and _HistoryDiabetes_ strongly contribute to predicting the presence of KD, aligning with the established clinical notion that diabetes is a significant risk factor for kidney disease. _HgbA1C_ is crucial for monitoring long-term blood glucose control, directly impacting kidney health in diabetic patients. Similarly, _eGFRBaseline_ is a highly relevant feature, reflecting the clinical practice of using eGFR to assess KD progression. _HistoryCHD_ also makes a significant contribution, as cardiovascular health is closely linked with kidney function in KD patients. Figure 14: LIME model global feature importance score using the ET classifier for KD prediction. Interestingly, features such as _ACEIARB_, a class of drugs commonly used to protect kidney function, and _TimeToEventMonths_, reflecting disease progression, were associated with lower predictions for CKD risk, consistent with their use in slowing kidney disease progression. The LIME results highlight the importance of these key features in helping clinicians interpret and refine diagnostic and treatment strategies.