Decision tree discovery for the diagnosis of type II diabetes

The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is to extract knowledge from information stored in database and generate clear and understandable description of patterns. In this study, decision tree method was used to predict patients with developing diabetes. The dataset used is the Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. The study goes through two phases. The first phase is data preprocessing including attribute identification and selection, handling missing values, and numerical discretization. The second phase is a diabetes prediction model construction using the decision tree method. Weka software was used throughout all the phases of this study.