## 4 Results
Comparatively, the results of the modeling process yield a superior classification capability with sensitivity (lower band) and specificity (upper band) values of (0.82, 0.88), (0.71, 0.80), and (0.60, 0.69) for the MLP, SVM, and LR models, respectively (Table 2). The model performance based on the area under the ROC curve analysis produced an overall accuracy value of 0.85, 0.76, and 0.64 for the MLP, SVM, and LR models with a standard error of 0.017, 0.021, and 0.024, respectively, at 95% confidence interval. The results of the hospital site suitability map are presented in Figure 6. The maps were generated from the effective conditioning factors with a relative influence greater than zero (in Table 1) to determining potentially suitable sites in which to locate hospitals in the study area. The modeling process exploits the interaction between these indicators (independent variables) based on the sampled dataset to establish a relationship that accurately produces the hospital site suitability map using SVM, MLP, and LR models. In each map, the degree of susceptibility is categorized into five classes: very high, high, moderate, low, and very low (Figure 6), utilizing the quantile classification method [13]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **95\% Confidence Interval** \\ \hline
**Model** & **AUC** & **Std Error** & **Lower Bound** & **Upper Bound** \\ \hline MLP & 0.849 & 0.017 & 0.816 & 0.883 \\ SVM & 0.756 & 0.021 & 0.714 & 0.798 \\ LR & 0.644 & 0.024 & 0.596 & 0.692 \\ \hline \multicolumn{4}{c}{**10-Fold cross-correlation method**} \\ \hline  & **R2** & **MAE** & **RMSE** & **RAE(\%)** & **RRSE(\%)** \\ \hline MLP & 0.925 & 0.070 & 0.231 & 13.62 & 25.20 \\ SVM & 0.940 & 0.011 & 0.001 & 4.18 & 12.23 \\ LR & 0.751 & 0.254 & 0.310 & 19.73 & 40.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model validation result: area under the curve and CFS 10-fold cross correlation. Figure 5: Comparative plot of the ROC curve for MLP, SVM, and LR. Figure 6: \(Cont.\)  
all the suitability classes across the study area. However, the central region, a section of the southern part, and small portion in the north indicate very high suitability levels. Moreover, the SVM-generated map (Figure 6b) is biased to very high (in small areas), high, and moderate classes, whereas the low and very low classes rarely exist. Unlike the other maps, the LR map product shows a distinct pattern (Figure 6c); the study area appears divided into two where the very high and high suitability classes occupy the southern section and the low and very low classes, interposed with the moderate class, are in the central and northern part of the Gaza Strip. The quantitative analysis has shown that for the MLP, the very high, high, and moderate suitability classes cover 10.10%, 27.74%, and 29.82%, approximately 36.9 km\({}^{2}\), 101.25 km\({}^{2}\), and 108.8 km\({}^{2}\), respectively. Moreover, the low and very low classes take up 21.62% (78.9 km\({}^{2}\)) and 10.72% (39.15 km\({}^{2}\)). For the suitability map produced with the SVM model (Figure 6b), the very high (3.65%), low (9.85%), and very low (0.35%) suitability classes represent a relatively very small percentage of the total area (\(\sim\)13.85%) which constitutes 50.5 km\({}^{2}\). Meanwhile, the larger percentage of the area is shared between the high and moderate suitable classes (36.38 and 49.77% respectively),, amounting to 314.5 km\({}^{2}\) with the latter occupying nearly half of the study area. Moreover, for the LR-generated suitability map (Figure 6c), the model predicts spatial coverage of 10.78%, 15.02%, 23.47%, 32.61%, and 18.12% for the very high, high, moderate, low, and very low suitability class, respectively, representing 39.35 km\({}^{2}\), 54.80 km\({}^{2}\), 85.65 km\({}^{2}\), 119.10 km\({}^{2}\), and 66.10 km\({}^{2}\), respectively