## 1 Introduction  
Nowadays, there are a multitude of decision support systems that organizational leaders use to make better decisions. These systems are used in numerous sectors, and many of these applications have been documented in the literature. There are also cases where significant research has been carried out using data mining techniques. Data mining is geared towards exploring data and finding solutions to particular business issues. Data mining, often also referred to as knowledge discovery in databases (KDD), is a sub-discipline of computer science aiming at the automatic interpretation of large datasets [1]. The classic definition of knowledge discovery by [PERSON] et al. from 1996 describes KDD as the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data [2]. The application of data mining in healthcare has increased in recent years because the healthcare sector is rich in information. Healthcare centres generate and collect large volumes of information on a daily basis. Despite this large volume of health and patient care information, it remains largely under-utilized [3], although it has been progressively applied in healthcare to assist clinical diagnoses and disease predictions [1]. In fact, recent studies have shown that most of the data mining work in the health sector is used in the analysis of diseases and pandemics [4]. Nevertheless, this information has been known to be rather complex and difficult to analyse. The proper management and exploitation of health information must be used to improve patient care and optimize healthcare resources. In this sense, the use of information technology allows the automation of data mining, extracting knowledge and generating interesting patterns of data behaviour. However, this research has remained at the theoretical level. The introduction of data mining in the production sector often results in complex applications with unfriendly user interfaces, where only the developers are able to operate them. As a result, these systems are not used. In order for these types of tools to be successful, it is essential to have a decision support system with a user-friendly interface [5; 6; 7; 8]. However, these developments are often not implemented due to their high-cost and complex development. Timeliness is recognized as an important characteristic of service delivery in any service setting, especially in health care. Some authors emphasize the importance of time to ensure efficiency in medicine [9]. From the standpoint of the patient, an example of the lack of efficiency is the difficulty to acquire an appointment with a physician in a timely manner, particularly at certain times of the year. This is a widespread issue in primary healthcare. Patient delays and long waiting times are one of the main problems faced by physicians and the rest of the health workforce in primary care clinics. This situation implies great difficulties for medical staff when attending to patients in a correct manner. Already in 1996, the work of [PERSON] [10] suggested that strong primary care resulted in higher patient satisfaction scores, lower healthcare expenses, and reduced drug prescriptions. At the moment, dimensioning of clinic resources is usually estimated from average generalized data of previous years. Therefore, despite of the number of patients who come to the doctor's office, the available resources are always the same. For the health centre managers, this criterion of sizing resources does not lead to waiting times collapsing on most days. However, in terms of healthcare demand, the accumulation of patients in waiting rooms should be avoided as much as possible, as the risks involved are well-known. The health centre must always have the necessary resources based on patient demand. In this sense, in knowing in advance the number of patients that will go to the health centre, it would be possible to prepare the medical and human resources needed to provide a quality service.