Langarizadeh Mostafa, Orooji Azam
Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Science, Tehran, Iran.
Med J Islam Repub Iran. 2018 Sep 12;32:85. doi: 10.14196/mjiri.32.85. eCollection 2018.
In recent years, liver disorders have been continuously increased. Proper performance of data mining techniques in decision-making and forecasting caused to use them commonly in designing of automatic medical diagnostic systems. The main aim of this paper is to introduce a classifier for diagnosis of liver disease that not only has high precision but also is understandable and has been created without expert knowledge. In regards to this purpose, fuzzy association rules have been extracted from dataset according to fuzzy membership functions which determined by fuzzy C-means clustering method; while each time, extracting fuzzy association rules, one of the five quality measures including confidence, coverage, reliability, comprehensibility and interestingness is used and five fuzzy rule-bases extracted based on them. Then, five fuzzy inference systems are designed on the basis of obtained rule-bases and evaluated in order to choose the best model in terms of diagnostic accuracy. The proposed diagnostic method was examined using data set of Indian liver patients available at UCI repository. Results showed that among considered quality measures, interestingness, reliability and truth outperformed respectively, and yielded precision, sensitivity, specificity and accuracy of more than 90%. In this paper, a classification method was developed to predict liver disease which in addition to high classification accuracy, it has been created without expert knowledge and provided an understandable explanation of data. This method is convenient, user friendly, efficient and requires no expertise.
近年来，肝脏疾病的发病率持续上升。数据挖掘技术在决策和预测中的恰当应用使得它们在自动医疗诊断系统的设计中得到广泛使用。本文的主要目的是介绍一种用于诊断肝脏疾病的分类器，该分类器不仅具有高精度，而且易于理解，并且无需专家知识即可创建。为此，根据由模糊C均值聚类方法确定的模糊隶属函数，从数据集中提取模糊关联规则；每次提取模糊关联规则时，使用包括置信度、覆盖率、可靠性、可理解性和趣味性在内的五个质量度量之一，并基于它们提取五个模糊规则库。然后，基于获得的规则库设计五个模糊推理系统，并进行评估，以便在诊断准确性方面选择最佳模型。使用UCI库中提供的印度肝脏患者数据集对所提出的诊断方法进行了检验。结果表明，在所考虑的质量度量中，趣味性、可靠性和真实性分别表现出色，其精度、灵敏度、特异性和准确率均超过90%。本文开发了一种用于预测肝脏疾病的分类方法，该方法除了具有高分类准确率外，无需专家知识即可创建，并能对数据提供易于理解的解释。该方法方便、用户友好、高效且无需专业知识。
