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目的 :通过职业性肺癌高发人群多因素聚类分析并建立判别函数 ,判定职业人群中个体在已知类中的归类。方法 :用系统聚类法和逐步判别法对 2 7名肺癌高发人群的细胞水平变化、分子水平变化及其它好发因素进行分类并建立判别函数。结果 :共聚为 2类 ,3人被聚为肺癌高危个体类 ,2 4人被聚为肺癌发生危险度较低的另一类 ;经逐步判别 ,姐妹染色单体互换频率 (SCE)、P2 1进入了判别函数 ,分别建立了 2类的判别函数 :Y1=3.2 2 1×10 -3 ×P2 1+1.6 2 8×SCE - 14.70 9和Y2 =1.11× 10 -2 ×P2 1+0 .41×SCE - 44 .46。以此判别函数进行回代分析 ,表明回代率为 10 0 %。结论 :可以按多因素对肺癌高发人群进行聚类、判别分析 ,以数学模型对职业人群作出未来是否患癌的预报。
Objective : To determine the classification of individuals in the known classes among occupational populations by multi-factor cluster analysis of occupational high-risk populations and establishing discriminant functions. METHODS: Systematic clustering and stepwise discriminating methods were used to classify and establish the discriminant function in the changes of cell level, molecular level and other predisposing factors in 27 high-risk populations with lung cancer. RESULTS: Copolymerization was classified into 2 groups, 3 individuals were clustered into high-risk individuals of lung cancer, and 24 were clustered into another category with a lower risk of lung cancer; the sister chromatid exchange frequency (SCE) and P2 were determined stepwise. 1 Into the discriminant function, established two types of discriminant functions: Y1 = 3.2 2 1 × 10 -3 × P2 1 +1.6 2 8 × SCE - 14.70 9 and Y2 = 1.11 × 10 -2 × P2 1 + 0 . 41×SCE - 44.46. Using this discriminant function for back-analysis shows that the back-generation rate is 100%. Conclusion : The clustering and discriminant analysis of high incidence of lung cancer can be performed according to multiple factors, and the mathematical model can be used to predict whether the occupational population will have cancer in the future.