A Statistical Decision Tree Algorithm for Medical Data Stream Mining

The use of computational resources can improve the diagnosis of medical diseases as a second opinion. Due to the large amount of data obtained daily, incremental techniques have been proposed to process medical data stream. In this paper we present an incremental decision tree classifier called StARMiner Tree (ST), which is based on Very Fast Decision Tree (VFDT) technique, to mine medical data. Different from VFDT, our proposed method ST does not depend on the number of reading samples to split a node. Because of it, ST is less conservative and describes the data since their first samples, being appropriate to be employed in medical environment, where not always a large number of data samples are available. We applied ST to four medical datasets, comparing the ST performance to the VFDT. The results indicated that ST is well-suited to deal with medical data streams, presenting high accuracy and low execution time.