Department of Information Management, National Yunlin University of Science and Technology, 123 University Rd., Section 3, Douliou, Yunlin 640, Taiwan.
Arch Gerontol Geriatr. 2012 Jan-Feb;54(1):232-7. doi: 10.1016/j.archger.2011.02.007. Epub 2011 Mar 5.
This study collected the real HD-data from area scale hospital database with 72 attributes and 18,113 records. The study proposes a novel procedure to assess the patient's HD-quality, including five facets: (1) Delete the unrelated attributes and missing values. (2) Employ expert granularity to cut decision-attributed Kt/V (where K is the dialyzer clearance coefficient of urea nitrogen, t is the time for dialysis and V is the urea nitrogen volume of distribution in the body). (3) Use information-gain to select features, to reduce the total number of attributes to 17. (4) Utilize multiple regression to test the degree of co-linearity and select features, the dimension of dataset is reduced to 8 attributes and 2737 records. (5) Finally, the rules of HD-quality and accuracy performance are generated by granular rough set theory. In performance comparison, the decision tree (DT-C4.5), the Naïve Bayes (NB) probabilistic model and Artificial Neural Networks-Multilayer Perceptrons (ANN-MLP) are employed to compare with the proposed procedure in accuracy. The results can assist doctors to reduce the time of diagnosis and to achieve dose of fitness-based dialysis for the patients.
本研究从区域规模医院数据库中收集了具有 72 个属性和 18113 条记录的真实 HD 数据。本研究提出了一种新的评估患者 HD 质量的程序，包括五个方面：（1）删除不相关的属性和缺失值。（2）采用专家粒度来切割决策属性 Kt/V（其中 K 是尿素氮的透析器清除系数，t 是透析时间，V 是体内尿素氮分布体积）。（3）使用信息增益选择特征，将属性总数减少到 17。（4）利用多元回归检验共线性程度并选择特征，数据集的维度减少到 8 个属性和 2737 条记录。（5）最后，利用粒状粗糙集理论生成 HD 质量和准确性性能的规则。在性能比较中，决策树（DT-C4.5）、朴素贝叶斯（NB）概率模型和人工神经网络-多层感知器（ANN-MLP）被用于与所提出的程序在准确性方面进行比较。这些结果可以帮助医生减少诊断时间，并为患者实现基于剂量的透析治疗。
