## 5 Empirical Evaluation: Setup and Findings
### Evaluation and Analysis of the Results
Both figures (a, and b) demonstrate the performance of the model for diabetes and non-diabetes (e.g., the unhealthy class and healthy class, respectively) classes. Figure 8a shows that the total average of the ROC curve for both classes is 0.91, while the total average of the ROC curve for both classes in Figure 8b is 0.90. Hence, Figure 8 indicates that the decision tree is a better model than the NB model by +0.01 due to the average ROC curve for both classes. From the receiver operating characteristic curve investigated in Figure 9, we can notice that the test set variation of the area under the curve for the TFKNN is more than Fuzzy KNN, which confirms the strength of the TFKNN classifier using this test set. However, the overall AUC performance from Table 10 shows that the TFKNN is the superior classifier for diabetes prediction on the PIDD. Figure 9 demonstrate the performance of the model for diabetes and non-diabetes (e.g., unhealthy class and healthy class, respectively) classes, such that Figure 9a shows that the total average of the ROC curve for both classes is 0.95, while the total average of the ROC curve for both classes in Figure 9b is 0.94. Table 10 demonstrates a comprehensive comparative analysis of different classification models. Each model is trained for the prediction of healthy versus unhealthy instances. Furthermore, Table 10 shows the micro-average ROC curve versus the macro-average ROC curve for different classification algorithms. From this table, it could be noticed that TFKNN achieves better rates than fuzzy KNN in micro- and macro-average ROC curve analysis. Depending on Table 9, we can confirm that TFKNN is better than other classifiers in average experiments instead of a single beating during a certain test set in the ROC curve analysis. Therefore, the prediction of diabetes based on the PIDD using a TFKNN classifier achieves good results compared with other traditional classifiers. \begin{table}
\begin{tabular}{c c c c c c|c|c} \hline
**Classifier** & **Precision** & **Recall** & **Accuracy** & **Specificity** & **Confusion Matrix** & **Average AUC** \\ \hline
**Naïve Bayes** & 85.71 & 88.64 & 81.89 & 66.67 & 78 & 13 & 89.92 \\ \cline{3-8}  & & & & & 10 & 26 & \\ \cline{3-8}  & & & & & 78 & 14 & \\ \cline{3-8}  & & & & & 9 & 26 & 90.76 \\ \hline
**Fuzzy KNN** & 91.30 & 95.45 & 90.55 & 79.49 & 84 & 8 & \\ \cline{3-8}  & & & & & 4 & 31 & 90.55 \\ \hline
**Proposed TFKNN** & 93.18 & 93.18 & 90.63 & 85.00 & 82 & 6 & \\ \cline{3-8}  & & & & & 6 & 34 & 94.13 \\ \hline \end{tabular}
\end{table} 
Table 9: Evaluation analysis of the results (best results in bold). Table 11 has been designed to illustrate the empirical analysis of the noted results by different KNN classifiers, fuzzy KNN, uncertainty Fuzzy KNN, different decision tree, and different naive Bayes classifiers versus the tuned classifier in terms of precision, recall, accuracy, and specificity. The results have been investigated using the 80:20 hold-out validation method and 10-fold cross-validation method. Table 12 demonstrates the global superiority of the proposed uncertainty fuzzy KNN classifier in terms of precision, accuracy, and specificity. For recall, the fuzzy KNN has global superiority at 95.45% versus all others regardless of the validation method. Figure 8: (a) ROC curve of the DT classifier for PIDD and (b) ROC curve of the NB classifier for PIDD. Figure 9: (a) ROC curve of the fuzzy KNN classifier for PIDD. (b) ROC curve of the TFKNN classifier for PIDD.