Text Mining Method in the Field of Health

Abstract

Objective: Text mining which digitalizes textual data and enables them to be applied for text mining algorithms has a very important place in today's world. The aim of this study was to introduce the text mining method and to show its application on a subject in the field of health. Methods: The text mining method was applied to the documents obtained separately from the most frequently used Pubmed database under two different titles as "humanand-cancer" and "mouse-and-cancer", and then to the combined documents, through the Knime program. Afterwards, the document classification was made using K nearest neighbor (K-NN) algorithm. Results: The prominent words were "cell" and "cancer" in tag cloud graphs. In both documents, the words such as "cell", "cancer", "tumor", "patient", whose frequency values were high, were observed to be high rates in the analysis performed after the data was merged. It was found that 255 of 600 test documents belonged to the humanand-cancer class and the remaining belonged to the mouse-and-cancer class, and the accuracy classification was 56.6% for the human-and-cancer-documents and 62.6% for the mouse-and-cancer-documents according to the F-criteria. It was determined that the document classification estimation by the K-NN algorithm was relatively successful with a rate of 59.8% however Cohen's kappa value was 19.7%, meaning that the fit was of a slight level. Conclusions: It was recommended to use the text mining method and to generalize its use in order to obtain information quickly and reliably in the health field where there were numerous digital and printed documents.