In this paper we present an overview of the current research being carried out using the data mining techniques for the diagnosis and prognosis of various diseases. The goal of this study is to identify the most well-performing data mining algorithms used on medical databases. The following algorithms have been identified: Decision Trees, Support Vector Machine, Artificial neural networks and their Multilayer Perceptron model, Naïve Bayes, Fuzzy Rules. Analyses show that it is very difficult to name a single data mining algorithm as the most suitable for the diagnosis and/or prognosis of diseases. At times some algorithms perform better than others, but there are cases when a combination of the best properties of some of the aforementioned algorithms together results more effective.
Data Mining (DM), Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes, Genetic Algorithm, Logistic Regression, Healthcare Database, Diagnosis, Prognosis
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