Synergy effects between grafting and subdivision in Re-RX with J48graft for the diagnosis of thyroid disease

Numerous methods for diagnosing thyroid disease have been developed, but the majority of these are black-box models. By contrast, the Recursive-Rule Extraction algorithm with J48graft is a white-box model that can provide highly accurate and concise classification rules. However, the potential capabilities of Re-RX with J48graft in terms of rule extraction remain unknown. Therefore, the aim of the present study was to elucidate the synergy effects between grafting and subdivision in Re-RX with J48graft, which work effectively in combination to extract highly accurate and concise classification rules for the diagnosis of thyroid disease. In the present study, I demonstrate how grafting and subdivision can extract highly accurate and concise classification rules from the Thyroid dataset, which is a large and highly imbalanced dataset consisting of 7200 medical records classified as normally functioning thyroid, hypothyroidism, or hyperthyroidism. I also provide the theoretical explanation underlying the excellent synergy effects between the two processes. Re-RX with J48graft not only achieved the most accurate classification rules, but also extracted simple and concrete concise classification rules for majority class samples. In addition, compared with previous methods, Re-RX with J48graft extracted rules with fewer antecedents. The maximum accuracy of the extracted rules was very high, at 97.02%. These findings suggest that Re-RX with J48graft can extract highly accurate and concise rules, which could assist healthcare professionals in the diagnosis of thyroid disease and help improve the level of care. (C) 2017 Elsevier B.V. All rights reserved.