Chen You-Shyang
Department of Information Management, Hwa Hsia University of Technology, No. 111, Gongzhuan Rd., Zhonghe District, New Taipei City 235, Taiwan, ROC.
Med Biol Eng Comput. 2016 Jun;54(6):983-1001. doi: 10.1007/s11517-016-1482-0. Epub 2016 Apr 6.
The high prevalence and incidence of severe renal diseases exhaust constrained medical resources for the treatment of uremia patients. In addition, the problem of imbalanced-class data distributions induces negative effects on classifier learning algorithms. Hemodialysis is the most common treatment for uremia diseases due to the limited supply of donated organs available for transplantation. This study focused on assessing the adequacy of hemodialysis. The lack of available information represents the primary obstacle limiting the evaluation of adequacy, namely: (1) the imbalanced-class problem in a given dataset, (2) obeying mathematical distributions for a given dataset, (3) a lack of effective methods for identifying determinant attributes, and (4) developing effective decision rules to explain a given dataset. To address these issues for determining the therapeutic effects of hemodialysis in uremia patients, this study proposes a hybrid imbalanced-class decision tree-rough set model to integrate the knowledge of expert physicians, a feature selection method, imbalanced sampling techniques, a rough set classifier, and a rule filter. The method was assessed by examining the medical records of uremia patients from a medical center in Taiwan. The proposed method yields better performance compared to previously reported methods according to the evaluation criteria.
严重肾脏疾病的高患病率和发病率耗尽了用于治疗尿毒症患者的有限医疗资源。此外，数据类别分布不均衡的问题对分类器学习算法产生了负面影响。由于可用于移植的捐赠器官供应有限，血液透析是尿毒症疾病最常见的治疗方法。本研究重点评估血液透析的充分性。缺乏可用信息是限制充分性评估的主要障碍，即：（1）给定数据集中的数据类别不均衡问题；（2）给定数据集是否符合数学分布；（3）缺乏识别决定性属性的有效方法；（4）制定有效的决策规则来解释给定数据集。为了解决这些确定尿毒症患者血液透析治疗效果的问题，本研究提出了一种混合不均衡类别决策树 - 粗糙集模型，以整合专家医生的知识、特征选择方法、不均衡采样技术、粗糙集分类器和规则过滤器。通过检查台湾一家医疗中心的尿毒症患者病历对该方法进行了评估。根据评估标准，与先前报道的方法相比，所提出的方法具有更好的性能。
