The unequal distribution of healthcare services is a main obstacle to health equality achievement. The more balanced allocation of services, the more population have access to them. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility of Isfahan’s census blocks to four different types of health services, including hospitals, pharmacies, clinics, and medical laboratories. Regarding the nature of spatial accessibility, machine intelligent-based unsupervised clustering methods are utilized to analyze the spatial accessibility in the city. Initially, the study area was grouped into five clusters using three unsupervised clustering methods: K-Means, agglomerative, and bisecting K-Means. Then, the intersection of the results of the methods is considered to be the definitive results. Finally, using the definitive results, a supervised clustering method, KNN, was applied to generate the map of the spatial accessibility situation in the study area. The findings of this study show that 47%, 22%, and 31% of blocks in the study area have rich, medium, and poor spatial accessibility, respectively. Also, according to the study results, the health services development is oriented in a linear pattern along a historical avenue, Chaharbagh. Although the scope of this study was limited in terms of the supply and demand rates, this work puts forward information for researchers, planners, and policymakers aiming to improve accessibility to healthcare. As a recommendation for further research work, it is suggested that other influencing factors, such as the demand and supply rates, should be integrated into the method.
