Rule extraction using Recursive Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset
Abstract Diabetes is a complex disease that is increasing in prevalence around the world. Type 2 diabetes mellitus (T2DM) accounts for about 90 95% of all diagnosed adult cases of diabetes. Most present diagnostic methods for T2DM are black box models, which are unable to provide the reasons underlying diagnosis to physicians; therefore, algorithms that can provide further insight are needed. Rule extraction can provide such explanations; however, in the medical setting, extracted rules must be not only highly accurate, but also simple and easy to understand. The Recursive Rule eXtraction (Re RX) algorithm is a "white box" model that provides highly accurate classification. However, due to its recursive nature, it tends to generate more rules than other algorithms. Therefore, in this study, we propose the use of a rule extraction algorithm, Re RX with J48graft, combined with sampling selection techniques (sampling Re RX with J48graft) to achieve highly accurate, concise, and interpretable classification rules for the Pima Indian Diabetes (PID) dataset, which comprises 768 samples with two classes (diabetes or non diabetes) and eight continuous attributes. The use of this algorithm resulted in an average accuracy of 83.83% after 10 runs of 10 fold cross validation. Sampling Re RX with J48 graft achieved substantially better accuracy and provided a considerably fewer average number of rules and antecedents than the original Re RX algorithm. These results suggest that sampling Re RX with J48graft provides more accurate, concise, and interpretable extracted rules than previous algorithms, and is therefore more suitable for medical decision making, including the diagnosis of T2DM.