## 5 Empirical Evaluation: Setup and Findings
### Evaluation and Analysis of the Results  
This section is designed to discuss and analyze the results of the proposed algorithm as well as the compared state-of-the-art methods. Table 9 represents the experimental results of different ML classifiers. Each experiment is illustrated using different classification performance metrics, including precision, recall, accuracy, specificity, and area under the curve (AUC). However, each classifier is supported using a confusion matrix for the consolidation of the stated values. Each classifier is trained and validated using the PIDD in the training set and test set at 80% and 20%, respectively. Table 9 represents the results of TFKNN, fuzzy KNN, NB, and DT regarding the proposed workflow in Figure 6. Table 9 holds notable values of the fine-tuning in terms of precision, accuracy, recall, specificity, and AUC. More precisely, Table 9 demonstrates the validation results and compares the different behaviors of the model using different validation test sets. Due to the AUC, the proposed TFKNN classifier achieves the highest table average value of 94.13, exceeding the nearest classifier by +3.37. Hence, the proposed TFKNN is considered an excellent classification model that can diagnose patients with the disease or condition based on the test. Since precision and recall are generally inversely \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Classification of Diabetes** & **Classification of Healthy** & **Total** \\  & **in Pregnant Women** & **Pregnant Women** & **Total** \\ \hline Actual diabetes in & & & \\ pregnant women & True Positive (TP) & False Positive (FP) & P \\ \hline Actual healthy & & & \\ pregnant women & False Negative (FN) & True Negative (TN) & N \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification matrix elements of the PIDD. \begin{table}
\begin{tabular}{c c} \hline \hline
**Measures** & **Formula** & **Description** \\ \hline Accuracy & (TP + TN)/(N + P) & The number of pregnant women correctly classified as having diabetes or not having diabetes for each instance. \\ \hline Specificity & (TN)/(TN + FP) & The number of samples correctly classified as non-diabetes in pregnant women for each non-diabetes instance. \\ \hline Precision & (TP)/(TP + FP) & The number of samples correctly classified as diabetes in pregnant women for each diabetes instance. \\ \hline Recall & (TP)/(TP + FN) & The ratio correctly identified as diabetes in pregnant women out of all diabetes instances. \\ \hline F1 score & \(2\times\) ((Precision \(\times\) Recall)/(Precision + Recall)) & The harmonics mean of the model’s recall and precision. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation metrics definition and the description. related, the TFKNN achieves a high precision rate and a low recall percentage, indicating the model's ability to discriminate diabetes patients from non-diabetes patients, achieving the intended objective of the overall analysis. In contrast, the fuzzy KNN model investigates recognizing non-diabetes cases versus diabetes cases. Hence, we can observe the benefits of noticed accuracy rates where the proposed TFKNN achieves 90.63% as the best classifier beating the rates of the decision tree, fuzzy KNN, and NB. The major objective of the ML model is rejecting healthy patients and accepting unhealthy patients without a condition. Regarding the specificity proportion, the proposed TFKNN achieves the highest rate for correctly rejecting healthy patients without a condition versus others. Even though decision trees and NB do not compete for classifiers over precision, recall, specificity, and accuracy, they compete well over the average AUC. The decision tree is considered the second most excellent model in discriminating healthy and unhealthy patients, and the NB model sets in the tail with a minor differentiation by 4.21 from the TFKNN classifier. Figure 8 illustrates the performance of the decision tree model versus the performance of the NB model.