## 4 Experimental Study
### Classifier Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} & \multicolumn{4}{c}{**AUC (\%)**} & \multicolumn{4}{c}{**F1-Score (\%)**} \\ \cline{3-14}  & \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**MC**} & \multirow{2}{*}{**XGB**} & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**MC**} & \multirow{2}{*}{**XGB**} & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**Base**} & \multirow{2}{*}{**MC**} & \multirow{2}{*}{**XGB**} & \multirow{2}{*}{**RF**} \\  & & **(165)** & & & & & & & & & & & \\ \hline \multirow{8}{*}{Baseline} & XGB & 96.78 & 97.75 & 97.72 & 97.74 & 97.83 & 98.48 & 98.47 & 98.46 & 97.24 & 97.95 & 97.96 & 97.97 \\  & KNN & 40.09 & 77.47 & 66.13 & 66.38 & 58.50 & 83.56 & 75.74 & 75.92 & 44.44 & 79.71 & 70.68 & 70.83 \\  & DT & 93.81 & 94.07 & 93.96 & 93.93 & 96.92 & 97.05 & 97.04 & 96.94 & 95.87 & 96.17 & 95.92 & 96.05 \\  & NB & 48.21 & 53.33 & 48.21 & 53.14 & 78.23 & 84.29 & 79.37 & 83.43 & 63.75 & 70.40 & 64.29 & 69.26 \\  & RF & 95.42 & 97.26 & 96.91 & 97.25 & 97.29 & 97.26 & 98.04 & 98.21 & 97.49 & 97.74 & 97.68 & 97.91 \\ \hline \multirow{8}{*}{Proposed model **} & MAH\_XGB & 97.79 & 97.98 & 97.92 & 97.76 & 98.52 & 98.64 & 98.61 & 98.47 & 97.99 & 98.21 & 98.17 & 97.94 \\  & MAH\_KNN & 41.93 & 78.71 & 67.69 & 67.48 & 59.53 & 84.39 & 76.76 & 76.59 & 44.12 & 79.71 & 70.81 & 70.98 \\ \cline{1-1}  & MAH\_DT & 94.51 & 95.31 & 95.05 & 94.13 & 97.17 & 97.58 & 97.46 & 97.03 & 96.34 & 96.86 & 96.63 & 96.04 \\ \cline{1-1}  & MAH\_NB & 54.94 & 60.92 & 55.71 & 64.60 & 79.45 & 85.27 & 80.47 & 83.81 & 66.12 & 71.96 & 67.68 & 72.65 \\ \cline{1-1}  & MAH\_RF & 96.67 & 97.62 & 97.29 & 97.25 & 97.96 & 98.43 & 98.28 & 98.22 & 97.51 & 97.93 & 97.94 & 97.74 \\ \hline \hline \end{tabular}  
* The baseline model was tested using the dataset in Figure 2 and without outlier removal. ** The proposed model was tested using the target dataset in Figure 2 with outlier removal.  
\end{table} 
Table 5: Evaluation comparison of the proposed methods on the target dataset based on training 70% and testing 30%. The ROC curves for each comparative method on the integrated dataset with k = 10-fold cross-validation are shown in Figure 5. When the training set is divided into several subsets, it is feasible to compute the mean area under the curve and view the variance of the curve for 10-fold cross-validation. The diabetes risk prediction accuracy of the dataset was compared using various machine-learning algorithms, as shown in Table 7. The MAH technique enhances the performance of the individual algorithms, and MAH_XGB has emerged as the most effective. The accuracy of the standard XGB algorithm, standing at 98.04% (with a 95% confidence interval (95% CI) of 97.89 to 98.59) with the MAH-based approach, is shown in Table 7. The results of our study indicate that XGB, followed closely by RF and DT, significantly outperforms other algorithms, such as KNN and NB. These algorithms exhibit minimal or even slightly declining improvements in accuracy when using these features. Figure 4: ROC curves for algorithm performance of the target dataset based on Table 5: (**a**) baseline models were tested using the dataset in Figure 2; (**b**) models enhanced by the proposed MAH-based outlier removal method. Figure 5: ROC curves for algorithm performance of the target dataset with k = 10-fold based on Table 6: (**a**) baseline models were tested using the dataset in Figure 2; (**b**) models enhanced by the proposed MAH-based outlier removal method using the target dataset in Figure 2.