# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 5 Experimental Results
### Explainable SHAP Analysis
However, _HistoryObestity_, one of the ten plot elements, has a negative impact. Figure 11b shows Patient ID 10's predictive probability is 1, comparable with that of Patient ID 0. Features such as _Age_, _HistoryDiabetes_, _eGFRBaseline_, and _DMmeds_ significantly influence the final prediction. Figure 11c shows that Patient ID 5 has a predicted probability value [f(x)] of zero. It shows that almost every feature hurts the prediction. However, _eDGRBaseline_, _TimeToEventMonths_, and _DMmeds_ make negative model predictions. According to the SHAP analysis, various features were consistently found as having considerable predictive potential across various patient situations. _TimeToEventMonths_, _DMmeds_, _eGFRBaseline_, _ACEIARB_, and _Age_ have been identified as significant predictors of KD development, supporting relevant clinical knowledge. For example, _TimeToEventMonths_ shows the time from diagnosis, which is essential in determining how far along a disease is. Similarly, _DMmeds_ (diabetic medicines) indicate the significance of managing concurrent medical conditions such as diabetes, a well-known risk factor for kidney disease. _eGFRBaseline_, an essential indicator of kidney function, is directly and rationally correlated with the severity of the disease. The _ACEIARB_ feature pertains to the usage of certain drugs that are often recommended to treat kidney-related complications, especially in patients with KD. Lastly, _Age_ is a broad but essential risk factor, with a higher risk of KD being associated with older age. These factors play a vital role in clinical decision-making, meaning that the model's significance is practical and can be put into practice. Using these insights, clinicians may prioritize monitoring and modifying treatment strategies, particularly for patients with large values in these aspects. For example, they might increase the dose of _ACEIARB_ medicines or strengthen monitoring for patients whose illness stage is fast progressing. It is crucial to match these model predictions with clinical practices to improve patient outcomes. It will guarantee that the findings can be adequately transformed into actionable clinical decisions. Challenges in SHAP Implementation  
Although SHAP offers practical interpretability for feature significance, its use has several challenges. Computational efficiency is essential, mainly when dealing with vast amounts of data or complex models. KernelSHAP, in particular, demands significant  
Figure 11: SHAP waterfall plots for three randomly selected patients, illustrating how each feature influences the final prediction. (**a**,**b**) show cases with high predictive probability, while (**c**) represents a case with a low probability. computing time, which might slow down analysis. However, Tree-SHAP improves this for tree-based models. Feature correlations also impact SHAP values, making it challenging to distinguish between independent feature contributions. Feature grouping and dependent plots were utilized to overcome this by enhancing interpretability. Additionally, domain expertise is necessary to convert SHAP results into clinically actionable insights, as certain features with high SHAP values may not always correspond directly to anticipated medical knowledge. By overcoming these challenges, SHAP will continue to be a strong and trustworthy tool for interpreting models.