A data-mining approach to improving Polycythemia Vera diagnosis
This paper presents a data-mining approach to the extraction of new decision rules for Polycythemia Vera (PV) diagnosis, based on a reduced and optimized set of lab parameters. Ten laboratory and other clinical findings (eight parameters from the Polycythemia Vera Study Group (PVSG) criteria + sex and hematocrit (HCT)) on 431 PV patients from the original PVSG cohort, and records on 91 patients with other myeloproliferative disorders that can be easily misdiagnosed with PV, were included in this study. Significant differences were not found in the correctness of diagnostic classification of patients using either a trained artificial neural network (98.1%) or a support vector machine (95%) versus using PVSG diagnostic criteria, which are considered as a 'gold-standard' for the diagnosis of PV. Reducing the original parameters of our dataset to only four parameters: HCT, PLAT, SPLEEN and WBC, we still have obtained good classification results. New rules for improved differential diagnosis of PV are specified based on these four parameters. These rules may be used as a complement to the standard PVSG criteria, particularly in the differential diagnosis between PV and other myeloproliferative syndromes. (C) 2002 Elsevier Science Ltd. All rights reserved.
polycythemia vera decision rules artificial neural networks support vector machines feature extraction n-dimensional visualization