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The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique

查看全文 作　　者：Yongqian [1]Qiang;Youmin [2]Guo;Xue [3]Li;Qiuping [1]Wang;Hao [3]Chen;Duwu [3]Cui 高影响力作者 机构地区：[1]Imaging Center, the First Affiliated Hospital, Xi' an Jiaotong University, Xi'An 710061, China;[2]Imaging Center, the Second Affiliated Hospital, Xi'an Jiaotong University, Xi'An 710004, China;[3]Computer Faculty, Xi'an University of Technology, Xi'An 710048, China高影响力机构 出　　处：《Journal of Nanjing Medical University》索引2007年第21卷第3期,共6页高影响力期刊 摘　　要：Objective: To discuss the clinical and imaging diagnostic rules of peripheral lung cancer by data mining technique, and to explore new ideas in the diagnosis of peripheral lung cancer, and to obtain early-stage technology and knowledge support of computer-aided detecting(CAD). Methods: 58 cases of peripheral lung cancer confirmed by clinical pathology were collected. The data were imported into the database after the standardization of the clinical and CT findings attributes were identified. The data was studied comparatively based on Association Rules (AR) of the knowledge discovery process and the Rough Set (RS) reduction algorithm and Genetic Algorithm(GA) of the generic data analysis tool (ROSETTA), respectively. Results: The genetic classification algorithm of ROSETTA generates 5 000 or so diagnosis rules. The RS reduction algorithm of Johnson`s Algorithm generates 51 diagnosis rules and the AR algorithm generates 123 diagnosis rules. Three data mining methods basically consider gender, age, cough, location, lobulation sign, shape, ground-glass density attributes as the main basis for the diagnosis of peripheral lung cancer. Conclusion: These diagnosis rules for peripheral lung cancer with three data mining technology is same as clinical diagnostic rules, and these rules also can be used to build the knowledge base of expert system. This study demonstrated the potential values of data mining technology in clinical imaging diagnosis and differential diagnosis. 关 键 词：周围型肺癌 诊断标准 数据挖掘技术 CT 计算机辅助检测
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