布鲁氏菌病是人类常见的人畜共患传染病。这种细菌感染是从受感染的动物及其产品中获得的。这种疾病的病原体是一种名为布鲁氏菌的杆菌属,目前还没有发现预防人类布鲁氏菌病的有效疫苗。
本研究主要是为了准确、及时地诊断布鲁氏菌病,使用数据挖掘技术。根据数据挖掘发现的知识和专科医师的意见,本研究旨在为布鲁氏菌病的诊断提供指导。
本研究中使用的数据集包含340个样本,并从2010-2020年德黑兰伊玛目霍梅尼医院的患者档案中提取。该数据集的属性已根据领域专家意见确定,即专科医师。经过初步分析和数据预处理,各种数据挖掘技术已被用来诊断布鲁氏菌病,包括神经网络,贝叶斯网络,和决策树。
根据记录的数据,270人(约占样本的79%)患有布鲁氏菌病。一些临床症状在感染患者中更为突出,包括发烧,关节炎,震颤,食欲下降,每晚出汗。在本研究中使用的所有数据挖掘技术中,采用C5.0剪枝算法的决策树在诊断布鲁氏菌病患者中具有最高的准确率(准确率约为99%).基于获得的最终模型,诊断布鲁氏菌病最重要的因素是赖特试验,库姆斯·赖特测试,血培养试验,和居住的地方。
根据这项研究的结果,使用数据挖掘技术可以高精度地诊断布鲁氏菌病。此外,通过数据挖掘可以确定诊断布鲁氏菌病的最重要因素。在这项研究的所有研究技术中,C5.0剪枝算法的决策树在诊断布鲁氏菌病方面具有最高的准确性。给定C5.0算法创建的决策树和专科医生的意见,一些指令是基于一个决策框架提出的分类对象为病人和非病人组.这些指令可以加速诊断,降低治疗成本,缩短治疗期。
Brucellosis is a common zoonotic infection of humans from livestock. This bacterial infection is acquired from infected animals and their products. The pathogen of this disease is a genus of bacilli called Brucella, and no effective vaccine has been discovered yet for the prevention of human brucellosis.
The present study is mainly conducted to diagnose brucellosis accurately and timely, using Data Mining techniques. Based on the knowledge discovered with Data Mining and opinions of specialist physicians, this study aims to propose instructions for diagnosing brucellosis.
The dataset used in this study contains 340 samples and is extracted from the files of patients at Tehran Imam Khomeini Hospital from the years 2010-2020. Attributes of this dataset have been determined based on domain expert opinions, namely specialist physicians. After initial analysis and data pre-processing, various Data Mining techniques have been employed to diagnose brucellosis, including neural networks, Bayesian networks, and decision trees.
According to the recorded data, 270 people (approximately 79% of samples) had brucellosis. Some clinical symptoms were more prominent among infected patients, including fever, arthritis, tremor, decreased appetite, and nightly perspiration. Among all employed Data Mining techniques in this study, the decision tree with C5.0 pruning algorithm possessed the highest accuracy in diagnosing patients with brucellosis (approximately 99% accuracy). Based on the obtained final model, the most important factors for diagnosing brucellosis are the Wright test, Coombs Wright test, blood culture test, and living place.
According to the results of this study, brucellosis can be diagnosed with a high accuracy using Data Mining techniques. Furthermore, the most significant factors for diagnosing brucellosis disease can be identified by Data Mining. Among all investigated techniques in this study, the decision tree with C5.0 pruning algorithm has the most accuracy in diagnosing brucellosis. Given the decision tree created by the C5.0 algorithm and the opinions of specialist physicians, some instructions are proposed based on a decision-making framework to classify referents into patient and non-patient groups. These instructions can accelerate the diagnosis, reduce therapeutic costs, and decrease treatment period.