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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Optimizations of the na?ve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data
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Optimizations of the na?ve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data

机译：融合流式细胞术数据的朴素贝叶斯分类器对B型慢性淋巴细胞性白血病预后的优化

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摘要

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 na?ve-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.
机译：B慢性淋巴细胞性白血病（B-CLL）的预后仍然是医学研究和实践中的难题。尽管通过流式细胞仪分析获得的参数构成了疾病诊断的基础，但这些参数是否提供其他预后信息的问题仍然悬而未决。在这项工作中，我们尝试通过部署B-CLL多参数预后分类系统，将与疾病相关的各种异质（临床，实验室和流式细胞术）参数结合在一起，从而为该领域的临床专家提供计算机辅助支持。为此，我们采用了朴素贝叶斯分类器并提出了一种提高其性能的算法。该算法离散化连续分类属性（候选预后参数），并选择它们中最有用的子集以优化分类准确性。因此，除了实现高分类精度外，所提出的方法还为预后提供了最有用的参数。实验结果表明，在我们的系统中包括流式细胞仪参数可以改善预后。

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