使用分类与回归树对红斑鳞屑性疾病进行鉴别诊断
Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree.
作者信息
Maghooli Keivan, Langarizadeh Mostafa, Shahmoradi Leila, Habibi-Koolaee Mahdi, Jebraeily Mohamad, Bouraghi Hamid
机构信息
Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
出版信息
Acta Inform Med. 2016 Oct;24(5):338-342. doi: 10.5455/aim.2016.24.338-342. Epub 2016 Nov 1.
INTRODUCTION
Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose.
OBJECTIVE
we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD.
METHODS
we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model.
RESULTS
The proposed model had an accuracy of 94.84% (.
STANDARD DEVIATION
24.42) in order to correct prediction of the ESD disease.
CONCLUSIONS
Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD.
引言
红斑鳞屑性疾病（ESD）的鉴别诊断是皮肤科领域的一项重大挑战。ESD疾病分为六个不同类别。数据挖掘是检测隐藏模式的过程。对于ESD而言，数据挖掘有助于我们预测疾病。为此开发了不同的算法。
目的
我们旨在使用分类回归树（CART）来预测ESD的鉴别诊断。
方法
我们使用了跨行业数据挖掘标准流程（CRISP-DM）方法。为此，从UCI机器学习库中获取了皮肤病数据集。使用IBM公司的Clementine 12.0软件进行建模。为了评估模型，我们计算了模型的准确性、敏感性和特异性。
结果
所提出的模型在正确预测ESD疾病方面的准确率为94.84%（标准差：24.42）。
结论
结果表明使用该分类器可能会有用。但是，强烈建议在ESD预测方面，机器学习方法的组合可能会更有用。
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