Optimizations of the naïve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data

Authors:
John Lakoumentas;John Drakos;Marina Karakantza;George Sakellaropoulos;Vasileios Megalooikonomou;George Nikiforidis
Affiliations:
Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Rion, Greece;Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Rion, Greece;Department of Internal Medicine, School of Medicine, University of Patras, GR-26504 Rion, Greece;Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Rion, Greece;Computer Engineering and Informatics Department, Polytechnic School, University of Patras, GR-26504 Rion, Greece;Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Rion, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

Prognosis of B-Chronic Lymphocytic Leukemia (B-CLL) remains a challenging problem in medical research and practice. While the parameters obtained by flow cytometry analysis form the basis of the diagnosis of the disease, the question whether these parameters offer additional prognostic information still remains open. In this work, we attempt to provide computer-assisted support to the clinical experts of the field, by deploying a classification system for B-CLL multiparametric prognosis that combines various heterogeneous (clinical, laboratory and flow cytometry) parameters associated with the disease. For this purpose, we employ the naive-Bayes classifier and propose an algorithm that improves its performance. The algorithm discretizes the continuous classification attributes (candidate prognostic parameters) and selects the most useful subset of them to optimize the classification accuracy. Thus, in addition to the high classification accuracy achieved, the proposed approach also suggests the most informative parameters for the prognosis. The experimental results demonstrate that the inclusion of flow cytometry parameters in our system improves prognosis.