摘要翻译：
本文提出了一种结核病的自动检测和分类方法。肺结核是一种由分枝杆菌引起的疾病，它通过空气传播，容易攻击低免疫力的身体。我们的方法是基于聚类和分类，将结核病分为两类，肺结核(PTB)和逆转录病毒肺结核(RPTB)，即那些伴有人类免疫缺陷病毒(HIV)感染的肺结核。首先使用K-均值聚类将TB数据分成两个簇，并为簇分配类。然后，基于K-折叠交叉验证方法，在结果集上训练多个不同的分类算法，建立最终的分类器模型。使用从一家城市医院获得的700份原始结核病数据对该方法进行了评估。与其他分类器相比，支持向量机(SVM)的最佳准确率为98.7%。建议的方法帮助医生在他们的诊断决定，也在他们的治疗计划程序不同类别。
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英文标题：
《A Data Mining Approach to the Diagnosis of Tuberculosis by Cascading
  Clustering and Classification》
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作者：
Asha.T, S. Natarajan and K.N.B. Murthy
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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英文摘要：
  In this paper, a methodology for the automated detection and classification of Tuberculosis(TB) is presented. Tuberculosis is a disease caused by mycobacterium which spreads through the air and attacks low immune bodies easily. Our methodology is based on clustering and classification that classifies TB into two categories, Pulmonary Tuberculosis(PTB) and retroviral PTB(RPTB) that is those with Human Immunodeficiency Virus (HIV) infection. Initially K-means clustering is used to group the TB data into two clusters and assigns classes to clusters. Subsequently multiple different classification algorithms are trained on the result set to build the final classifier model based on K-fold cross validation method. This methodology is evaluated using 700 raw TB data obtained from a city hospital. The best obtained accuracy was 98.7% from support vector machine (SVM) compared to other classifiers. The proposed approach helps doctors in their diagnosis decisions and also in their treatment planning procedures for different categories.
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PDF链接：
https://arxiv.org/pdf/1108.1045