Preliminary Study on Clinical and CT Diagnostic Rule of Peripheral Lung Cancer Based on Data Mining Technique
Cui Du-w · Shiyong fangshexue zazhi · 2007
Objective To discuss the extraction value about the imaging diagnostic rules of peripheral lung cancer by using data mining technique.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.The data were studied comparatively based on the knowledge discovery process in association with the rough set reduction algorithm and genetic algorithm of the generic data analysis tool(ROSETTA),respectively.Results The diagnostic rules generated by the rough set reduction algorithm of Johnson's Algorithm,the genetic classification algorithm of ROSETTA and the mining algorithm generates were 51,over 5000 and 123 respectively.The main items for the diagnosis of peripheral lung cancer generated by three data mining methods basically were gender and age of patients,the location,burr sign,shape and ground-glass density of lesions.Conclusion The data mining technology in medical imaging diagnosis and differential diagnosis is of the potential value.