## 5 Empirical Evaluation: Setup and Findings
### Discussion  
To present a comprehensive evaluation of the proposed fine-tuned classifiers, we have evaluated the dataset versus different recent works of literature,s including shallow classifiers and deep learning models. A comparative study is given in Table 12 between the state-of-art methods with the proposed fine-tuned hyperparameter model based on fuzzy KNN, DT, KNN, and NB. The comparative study depends on determining the confusion matrix precision, recall, F1 score, and accuracy for the applied PIDD dataset. Table 12 concludes the importance and impact of preprocessing efficiency on classification performance. As shown in Figure 10, based on the NB classifier, [PERSON] and [PERSON] [25] and [PERSON] et al. [29] achieved accuracies 76.30%, and 73.10%, respectively, compared with the proposed 81.89% NB accuracy. On the other hand, [PERSON] et al. [26] achieved an accuracy of 76.80% for the logistic regression (LR) classifier, compared with [PERSON] et al. [29], who achieved an accuracy of 73.30% with LR. Furthermore, [PERSON] and [PERSON] [25] introduced sequential minimal optimization, which achieved an accuracy rate of 77.34% versus 90.63% by fuzzy KNN and 90.55% by KNN for the proposed fine-tuned classifier. The improvement of the proposed algorithm is a result of perfect preprocessing, feature quality analysis, and hyperparameter optimization using the grid search algorithm. Figure 10 represents a comparative evaluation of the NB classifier using the PIDD regarding the accuracy metric. The proposed fine-tuned classifier achieves better accuracy by +6.39%, and the evaluation of the proposed records the testing output of the experiment. Figure 11 illustrates the behavior of the DTRF classifier regarding the accuracy, in which the proposed fine-tuned classifier achieves +4.89% better accuracy than the decision tree by [PERSON] and [PERSON] [27] and the classical decision tree random forest by [PERSON] et al. [26]. Figure 12 compares the evaluation of different KNN classifiers using the PIDD. The proposed method reaches a 90.55% accuracy rate, improving it by +17.39. Generally, the KNN classifier reaches higher accuracy rates than other classifiers. Hence, resolving the uncertainty issue of KNN leads to the improvement of the fuzzy KNN model. As investigated in Table 12, KNN is better than the fuzzy KNN in terms of the recall; however, this is not the major role of medicine assistance systems. However, the precision and F1 score are more confidential for correctly rejecting healthy patients without a condition versus others. In terms of precision, the fuzzy KNN is better than KNN, confirming the ability of our proposed model to discriminate diabetes patients from non-diabetes patients. Even though there are slight improvements in KNN over fuzzy KNN  
Figure 10: Comparative analysis of various configurations of NB using the PIDD. regarding the F1 score, overall, the accuracy of the fuzzy KNN is 90.63%, which is better than the 90.55% achieved by the KNN. The average value of the area under the curve (AUC) is 94.13% for fuzzy KNN and 90.55% for KNN, which proves the superiority of the fuzzy KNN classifier for uncertainty values. In this work, we have supported our study with comparison and analysis with some of the deep learning. After focused research using academic research engines, we have compared and analyzed some studies using the PIDD and different deep learning methods which were published recently [34; 36]. Furthermore, we have survived different available taxonomies for modeling diabetes using the PIDD. We further agree with the opinion about the comparison and analysis with some deep learning methods; however, let us demonstrate more about PIDD meta-data and its relation to deep learning methods. * In PIDD, the normal and abnormal instances are imbalanced, which increases the probability of model biasing;  
Figure 11: Comparative analysis of various configurations of decision tree algorithms using the PIDD. Figure 12: Comparative analysis for various configurations of KNN using the PIDD.