## 4 Results  
An assessment of the independent variables (conditioning factors) relative to the dependent variable (hospital location) using the correlation feature selection provides insight into the degree of importance of each conditioning factor to the overall model building. The CFS analysis ranks the parameters according to the correlation with the class label and the other parameters (Table 1). From the table, it can be observed that the relative influence between hospital locations and the other parameters showed that the population density and distance from the road had the highest values of CFS (100%), closely followed by the distance from the main road and the distance from the residential area (90%). The factors in the mid-range are the distance from agricultural land and population number (70%), slope, plan curvature, and no-go zone with a relative influence value of 60%, 50%, and 40%, respectively. Those with a low relative influence on hospital siting are altitude, the distance from the refugee camp (20%), and the distance from a river (10%). The result also reveals that SPI, TWI, and TRI have 0% influence and therefore were eliminated from the model building process. The CFS evaluation is based on merit, evaluated based on the coefficient of correlation and error rates [83]. In this study, we investigate the performance of the SVM, MLP, and LR models at different stages of the modeling process. The first is at the level of feature selection to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Relative Influence \%** \\ \hline Population density & Density of population & 100\% \\ Road & Distance from the road & 100\% \\ Main road & Distance from the main road & 90\% \\ Residential & Distance from the residential & 90\% \\ Agriculture & Distance from the agriculture & 70\% \\ Population & Populations number & 70\% \\ Slope & Slope degree & 60\% \\ Curvature & Plan curvature & 50\% \\ Prohibited zone & No-go zone & 40\% \\ Altitude & Altitude & 20\% \\ Refuge camp & Distance from the refugee camp & 20\% \\ River & Distance from the river & 10\% \\ TRI & Topographic roughness index & 0\% \\ TWI & Topographic wetness index & 0\% \\ SPI & Stream power index & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relative influence (%) of the effective parameters. appraise the conditioning factors relative to the models through an analysis of the cross-validation accuracy measures (correlation coefficient, MAE, RMSE, RAE, and RRSE). The second stage assesses the performance of the model, also determined using the ROC curve metrics; sensitivity, specificity, and area under curve (AUC) values. Table 2 presents detailed result of both of the stages of evaluation. The cross-validation result revealed a high correlation between the conditioning factors and hospital site for the models used (Table 2). SVM and MLP have a very high correlation with the R2 value of 0.94 and 0.93, respectively. However, the SVM has the lowest error rate with the MAE, RMSE, and RAE value of 0.011%, 0.001%, and 4.18%, respectively compared to 0.07%, 0.231%, and 13.62% for the MLP classifier. The LR model also indicates a moderately high correlation with the R2 value of 0.75 but with a slightly high error rate based on the reported MAE, RMSE, and RAE value of 0.25, 0.31, and 0.60, respectively. The cross correlation is a pre-modeling assessment of the applicability of the SVM, MLP, and LR models for predicting a suitable site to locate a hospital with the conditioning factors considered in the study. Figure 5 and Table 2 show the result of the model performance evaluation using the 30% validation (testing) dataset. The graph of sensitivity-specificity plots provides a visual and statistical understanding of how well the models can classify a suitable site for a hospital location from an unsuitable position. It can be seen from the figure that the ROC curves of the three models approach the upper left corner of the plot. This is interpreted to mean a high overall accuracy [84].