## 2 Background  
The site selection of public facilities is a strategic matter. The selection of a public facility site usually results in success or failure, especially a hospital [27]. The selection of a hospital location is theorized as a problem of multicriteria decision making that includes many criteria that can be conflicting and dependent, or independent [28]. Various studies have used geographic information systems (GIS) to select a proper site for constructing a new hospital. The analytical hierarchy process (AHP) developed by [PERSON] (1980) is one of the most valuable methods which plays a major role in the optimal preference selection [29; 30]. [PERSON] et al. [31] identified the optimal location of hospitals in Taiwan and performed a sensitivity analysis utilizing AHP together with a modified method of Delphi. Another study by [PERSON] and [PERSON] [32] implemented an expert system for the selection of the health utilities for ideal cities. The researchers utilized a combined system operated by the analytic network process (ANP) integrated with the TOPSIS method. [PERSON] et al. [33] suggested a combination method of GIS and a fuzzy AHP for hospital site selection in Tehran. Another research project used a fuzzy AHP (FAHP) to select a suitable site for constructing a new hospital in Ankara by [PERSON] [34]. [PERSON] and [PERSON] [35] proposed a two-stage method of fuzzy multicriteria. First, the authors assessed developable parcels using GIS and FAHP, while for the second, they employed a fuzzy ANP (FANP). Previous studies have demonstrated the use of several approaches to analyze datasets with a different application for different parameters [12; 13; 14; 36]. Among the methods used are correlation feature selection, multilayer perceptron, support vector machines, and linear regression models. These are machine learning's most common models, and yet they have not been applied in hospital site suitability which motivated their selection and application. Correlation feature selection (CFS) is a method of choosing a feature that uses subset evaluators and search algorithms to produce the most accurate subset for each dataset. In this analysis, the correlation feature selection subset evaluator and the search method of greedy stepwise were applied directly to the algorithm of the feature selection. The subset ranked the features according to the correlation with the class label and other features using the correlation feature selection subset evaluator (CfsSubsetEval). The subsets of the features that were highly correlated with the class label and less correlated with other features were classified as giving a higher value. Moreover, this method eliminates inappropriate and repetitive features of the dataset [37; 38; 39]. The greedy stepwise search begins from the blank set, then defines the variables through the forward selection, and also reduces the unwanted variables through the back selection to get the most accurate subset of the feature. A new set of nominee feature subsets forms by attaching additional features to the most accurate subset during the search process. After evaluating all of the subsets, the best feature subset is selected. The algorithm persists these processes until the newly created set of the subsets exceeds no more than the best existing subset [39; 40; 41]. The multilayer perceptron (MLP) is an important artificial neural network model that feeds sets of input data to a suitable set of outputs. The MLP model helps understand the system behavior according to input notes and can round values without prior knowledge of the data relationships [42]. MLP has many advantages, such as distributing training datasets that are not based on the presumptions, and no decision is required regarding the relevant importance of individual input measures, and most of the input measures are chosen according to the weight adjustment throughout the training phase [43]. MLP is comprised of various layers of nodes that are directed in lines, completely associated with each layer.