A data-mining approach to improving polycythemia vera diagnosis

Authors:
Mehmed Kantardzic;Benjamin Djulbegovic;Hazem Hamdan
Affiliations:
Computer Engineering and Computer Science Department, J.B. Speed Scientific School, University of Louisville, Louisville, KY;Division of Blood and Bone Marrow Transplant, H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL;Computer Engineering and Computer Science Department, J.B. Speed Scientific School, University of Louisville, Louisville, KY
Venue:
Computers and Industrial Engineering
Year:
2002

Citing 9
Cited 1

What size net gives valid generalization?

Neural Computation
Neural network training tips and techniques

AI Expert
The nature of statistical learning theory

The nature of statistical learning theory
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets

SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Logic-oriented model of artificial neural networks

Information Sciences: an International Journal
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms

Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms
Formulations of Support Vector Machines: A Note from an Optimization Point of View

Neural Computation
Visualization of neural-network gaps based on error analysis

IEEE Transactions on Neural Networks

Review: Knowledge discovery in medicine: Current issue and future trend

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.