## 4 Results and Discussion  
Figure 6 displays Area under the (ROC) Curve. The LR, SVM, DT, RF, KNN, and XGBoost models are represented by lines of a different color in the figure. The black curve's AUC value of XGBoost is somewhat higher than that of the other four curves, indicating that it has the best performance out of the five models. Figure 6: AUC for different ML algorithms. Initially, the few vital steps taken in the preprocessing stages are evaluated. The essential process is to assess the estimation accuracy of mean and mode methods in identifying the missing values over 400 considered instances. Thus, Figure 7a,b depicts the resultants of the process in comparison with the randomized manual verifications. The results precisely state that the average estimation accuracy of both mean and mode methods in identifying numerical and nominal missing values are 97.77% and 96.93%, respectively, which is 5.71% more accurate than the randomized manual verifications. The values in the confusion matrix for all ML algorithms being implemented is shown in Figure 8.  
Figure 8: ML algorithms—Confusion matrix. Figure 7: Missing value estimation accuracy using mean and mode method. Precision indicates what amount of the total positive anticipated is genuinely positive. The ratio of true positives (TPs) to total number of TPs and true negatives are called precision (TNs). Precision is a good statistic to utilize when the cost of false positives (FNs) is high. Using Equation (26), the precision is calculated. \[\text{Precision}=\frac{\text{TPs}}{\text{TPs}+\text{FPs}} \tag{26}\]  
The precision values of LR, SVM, KNN, DT, RF, and XGBoost classifiers are 96.3%, 95.69%, 91.18, 91.95, 93.10%, and 98.9%, respectively. Recall quantifies how many right positive predictions were made. The equation used to calculate the recall is (27) shown below. \[\text{Recall}=\frac{\text{TPs}}{\text{TPs}+\text{FNs}} \tag{27}\]  
Recall of LR, SVM, KNN, DT, RF, and XGBoost classifiers are 97.56%, 97.8%, 96.47%, 94.11%, 96.42%, and 98.9% respectively. The F1-score considers both recall and precision. F1-score is computed using the below Equation (28). \[\text{F1 Score}=2*\frac{\text{precision}*\text{recall}}{\text{precision}+ \text{recall}} \tag{28}\]  
The F1-score of the SVM, LR, DT, KNN, RF, and XGBoost classifiers is 96.73%, 96.93%, 93.01%, 94.75%, 94.73%, and 98.9%, respectively. The performance evaluation of these ML algorithms is shown in Figure 9.  
One technique to determine how often an ML classification system properly classifies a record or instance is looking at accuracy. The Equation (29) is used to compute accuracy. \[\text{Accuracy}=\frac{\text{TNs}+\text{TPs}}{\text{TNs}+\text{TPs}+\text{ FPs}+\text{FNs}} \tag{29}\]  
The performance of different ML Techniques for CKD classification is shown in Table 5 in terms of percentage. Figure 9: Different ML techniques—Performance evaluation. Table 6 shows the comparison of XGBoost performance with other ensemble techniques and XGBoost again outperforms with an accuracy of 98.33%. Figure 10 exposes the superior performance of the proposed EL method during the testing phase in the classification of CKD. Such test validation is required to monitor the development of the trained algorithm and fine-tune or maximize its performance. Therefore, the learnt logic of the EL technique is regularly evaluated on a sample size of about 100 instances, and the findings (average accuracy is 97.78%) acquired provide concise evidence that the system maintains ideal consistency and precision. This accuracy provides the correctly classified instances of CKD. The accuracy of different ML techniques in the classification of CKD is shown in Figure 8.0 It is noted that XGBoost exhibits the best prediction with an accuracy of 98.93% and outperforms