## 5 Discussion  
Determining an appropriate method to identify suitable locations to build a hospital is a difficult process that involves taking decisions on the appropriate conditioning factors and dealing with the spatial heterogeneity associated with them. Therefore, the first step to ensure a reliable result was to assess how related the chosen variables considered are to determining a potential hospital location by examining their relative influence and correlation with the models using the 10-fold cross-correlation approach. Through this process, the conditioning factors that have no contribution were excluded from the modeling process. Moreover, the correlation values obtained, 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR, respectively, shows that the variables considered are fit for the purpose. Top on the list of relative influence are population density and the distance from the road (100%), the distance from the main road and from residential areas (90%), the distance from agricultural land and population number (70%), while the slope, plan curvature, and no-go zone are in the average with a relative influence of 60%, 50%, and 40%, respectively. This indicates that human factors dominate the choice of location rather than topographic factors since the altitude of a place determines the slope, curvature, and other morphometric elements. The model performance evaluation is based on the ROC curve parameters (sensitivity, specificity, and AUC) measured on a standard scale of 0-1; where a value <0.6 indicates low accuracy, while those between 0.6-0.7, 0.7-0.8, 0.8-0.9, and >0.9 are interpreted to be in the accuracy range of either moderate, good, very good, or excellent [61]. The sensitivity and specificity metrics interactively provide insight into the classification capability of the models. Sensitivity, otherwise called the true positive, indicates how well a model can classify the sampled data to truly identify the ideal position to build a hospital, while the specificity (or true negative) indicates the model's ability to correctly classify unsuitable locations through the sample data. The sensitivity-specificity values obtained for the MLP model are 0.82, 0.88, SVM are 0.71, 0.80, and LR are 0.60, 0.69. According to [PERSON] and [PERSON] [84], on a scale of 0 to 1, the closer the value obtained is to 1, the better the capability of the model in classifying data. In this study, the overall performance evaluation produced AUC values of 0.85, 0.76, and 0.64 for the MLP, SVM, and LR model, which fall into very good, good, and moderate accuracy, respectively. Using the knowledge of the first author about the study area, combined with exploration of high-resolution Google Earth map, we observed that the result of the MLPtruly reflects the natural environmental setting of the study area (consistent with previous reports in the study area [54]). For example, the very high and high suitable area occurred in the cities with more population and easy accessibility (Figure 6a). Similarly, the low and very low suitable area appears in areas designated as a no-go zone, open land, and very sparsely populated area. In contrast, the SVM result (Figure 6b) predicts high and moderate classes in areas such as a no-go zone and open unoccupied areas ordinarily considered inappropriate as optimal locations. The map in Figure 6c (LR model) partitions the classes into upper, middle, and lower bands as a contiguous neighbor in a way that neither reflects the input data nor the reality on the ground. The application of SVM, MLP, and LR models in this study provides scientific evidence of the resourcefulness of multilayer (ML) techniques for a hospital site suitability assessment similar to the results obtained in other fields of study, such as flooding [14; 16; 17; 18], landslide [19; 20; 21], forest fire [22; 23], erosion, and water resources [24; 25; 26], etc. In this investigation, MLP and SVM perform satisfactorily with AUC values of 85% and 76%, respectively, compared to the LR model which produced poor results.