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    The criteria for classification tree methods in clinical researches
    (2010) Akkus Z.; Sanisoglu S.Y.; Ugurlu M.; Celik M.Y.
    This study aimed at evaluating a statistical method, classification tree, which are recently developed parallel to the improvements in computer technology. The advantages over other methods and the criterions developed for classification tree are reported in this study. Classification tree (CT) is a non-parametric statistical method using a tree algorithm for reaching diagnosis by utilizing one or more risk factors. Classifications (discriminative, logistic regression and cluster analysis etc) and regression methods are frequently employed in analysing data acquired from scientific studies. However, hypothesis in these models makes the statistical analysis limited to be performed in wide range of disciplines. As there is no need for hypothesis in analysing these data sets, classification trees are serious alternative for other statistical classification and regression techniques. Classification tree, also known as Decision tree, is a good choice for data mining classifications in respect to both understanding and explaning the some particular rules about estimating the results. These methods are evolved following the improvements in computer technology. Classification tree is becoming more important in practice as it provides reliable measures in building accurate classifications. The advantages of the method over others are the following: simplification of the results, provision of non-parametric and lineer solutions, generalization of the conclusions optained by inductive reasoning. More over the technique can utilize mixed data types and the same variable can be employed in different parts of the tree. The determination of choices, which is crucially important in accurate interpretation of the results, needs time and effort in practicing the method. In field of medicine, classification tree is one of the favorable methods particulary utilized in clinical studies.