In this study, a software tool was developed to analyse the medical data collected from laryngeal cancer operations by using two data mining techniques. The software, run on real-world medical data, is a tool that enables medical decisions to be reached by analysing past records from patients. The k-means algorithm, which is a clustering algorithm in data mining, was used to point out the intensities in the data set and to display two dimensions on the charts. The data of three screens that were named as selective clustering, different pre- and post-operation stages and clustering operations based on pre-operation T values, were processed using clustering with the k-means algorithm and one screen, which named relapse and survival percentages, was processed through classifying. It helps the future decision-making process by considering false estimates of pre-operation stages of the cases and by using the information gathered from past cases concerning tumour relapse and the survival percentage for prognostication. The characteristics of laryngeal cancer operations data, that involve causal links, were exposed by using two data mining techniques in this application.