# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 5 Experimental Results
### Explainable SHAP Analysis  
Evaluating the local and global interpretability of predictive models is imperative, especially in classification frameworks, where models related to prediction significantly impact performance. The ET algorithm is appropriate for current medical clinical data analysis because of its notable model performance and execution speed. SHAP offers a potent method for feature interpretability, and its waterfall charts, summary, and SHAP values are practical tools for explaining how each feature contributes to the model's output. Figure 9 shows the feature importance ranking in descending order using mean SHAP values for the ET model, with mean SHAP values on the x-axis and feature rankings on the y-axis. It indicates that _TimeToEventMonths_, _DMmeds_, _eGFRBaseline_, _ACELARB_, and _Age_ are the most important predictors. Features with higher SHAP values are more important in predicting outcomes, as the plot shows. Figure 10 shows the SHAP summary plots for both ET and RF models. The summary plots thoroughly explain how individual distinctive features contribute to the significance of the target variable in prediction. Features are sorted by importance on the y-axis, which shows the strength and direction of the feature correlation with the desired result, while the x-axis shows SHAP values. While negative numbers indicate an adverse effect, positive SHAP values positively impact the prediction. Every line in the plot represents a unique feature: higher feature values are shown in red, while lower values are indicated in blue. This color combination offers insights into the connection between feature values and prediction. Figure 8: Accuracy comparison of the proposed methodology with previous research [6]. Figure 10: SHAP summary plots for the machine learning classifiers, showing how individual features impact KD prediction. (**a**) SHAP-based ET model; (**b**) SHAP-based RF model. Figure 9: Feature importance ranking using mean SHAP values for the ET model for KD prediction (x-axis: SHAP values indicating each feature’s contribution to the model’s prediction; y-axis: features in descending order of importance). A comparison of the top five primary features of both models indicates substantial variations between them. The ET model prioritizes _TimeToEventMonths_, _DMmeds_, _eGFRBaseline_, _ACEIARB_, and _Age_, while the RF model highlights _TimeToEventMonths_, _DMmeds_, _eGFRBaseline_, _HgbA1C_, and _dBPBaseline_ as the most important predictors. This comparison shows similarities in major clinical parameters, including _TimeToEventMonths_, _DMmeds_, and _eGFRBaseline_, and variances in secondary relevant variables. Given that _ACEIARB_ is included in ET and _HgbA1C_ is included in RF, ET may emphasize medication-related aspects more, whereas RF gives metabolic indications more weight. This comparative SHAP analysis provides insights into the relevance of features across multiple classifiers and further verifies the interpretability of the ET model. It also shows how RF is based on predictive reasoning. The SHAP waterfall plot for the ET model is shown in Figure 11, where each feature's magnitude indicates its impact on the final prediction. The feature's effect on the model's output directly correlates with the bar size. The ET model's predictive probability was analyzed for three randomly selected patients, and the features' contribution to the prediction were plotted as shown in Figure 11. Positive impacts (shown in red) raise the projected outcome from the base value in a waterfall plot, while negative effects (shown in blue) lower it. Every step in the waterfall plot represents a different feature's contribution; the length of the step indicates the feature's size of impact, and the direction (up or down) indicates whether it raises or lowers the predictions. Figure 11a shows that Patient ID 0 has a predictive probability value [f(x)] of 1. The top positive contributors, such as _TimeToEventMonths_, _DMmeds_, _eGFRBaseline_, _HistoryCHD_, and _ACEIARB_, improve prediction.