## 2 Literature Review
### State of the Art  
[PERSON] and [PERSON] described Clinical Decision Support Systems as \"computer systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made\" [12]. Our literature review focuses on expert systems developed in order to assist the diagnostic step. In the last few years, expert systems for diagnosis and disease classification have been greatly increased, due to evolution of this area of research, the appearance of new algorithms and the successful reported performance of several proposals over the years. In comparison with the last few decades, systems focus on more specific diseases and organs lately, in order to reach better accuracy of results and overtake the great effort of obtaining a comprehensive system which determines its output between hundreds of diseases. Despite the progress of Artificial Intelligent systems in diagnostic areas, limitations such as scarcity of open datasets slow down the evolution of systems, since this prevent verification and validation of new proposed systems and comparison between different proposals. In this way, most of the proposals in literature with open datasets like Pima Indians Diabetes, Breast Cancer Wisconsin or the HeartDisease Dataset. The high complexity inherent in medical data has caused most of the systems to become hybrid in order to improve performance. Support Vector Machines (SVMs) were first proposed by [PERSON] [9]. SVMs classify new examples, mapping them into a higher dimensional input space. SVMs have been widely used, resulting from their ability in generalization and good experimental results. This technique has been recently applied to thyroid disease diagnosis [13], early prostate cancer diagnosis [14], where SVM obtained better results than Artificial Neural Networks, or urological dysfunctions diagnosis [15]. SVM also appears to be the most accurate technique in breast cancer classification, which has received much attention in recent years [16,17]. Artificial Neural Networks (ANNs) are inspired by the human brain. Neural network architectures are massively parallel and adaptive, giving an output when they receive an unknown input due to generalization from past experience. ANNs stand out in pattern recognition, but have the disadvantage that they don't explain its outcomes, working like a black box. Artificial neural networks have been widely used in decision support in cancer as showed by [PERSON] and [PERSON] [18]. Lately, neural networks are mainly exploited through hybrid systems that overcome issues derived from the absence of explanation of its outcomes, with neuro-fuzzy as a specially increasing area as reflected by [PERSON], [PERSON] and [PERSON] [19]. Fuzzy Logic reasoning employs linguistic rules, allowing the application of human language and therefore having the advantage that they provide a ready explanation of its models and outcomes. Fuzzy rule-based systems are used in situations with uncertainty and incompleteness of data, and can incorporate expertise and knowledge extracted from experts in the field. As previously mentioned, complementary properties between fuzzy logic and neural networks leads to widespread application of neuro-fuzzy hybrid systems. These systems have shown their good performance in diabetes diagnosis [20] or thyroid disease diagnosis [21]. Rules allow knowledge representation in expert systems. Advantages of rule-based systems are their simplicity and explanatory ability and the modularity of rule sets, which can be easily modified and extended. Some drawbacks are difficulties dealing with incompleteness of data or the effort required to incorporate expertise when constructing large models. One of the earliest medical expert system, MYCIN [10], provided consultation about infectious disease, diagnosis, and choice of therapy by means of a set of rules.