Development of diagnostic model of lung cancer based on multiple tumor markers and data mining.

ABSTRACT:

Objective

To develop early intelligent discriminative model of lung cancer and evaluate the efficiency of diagnosis value.

Methods

Based on the genetic polymorphism profile of CYP1A1-rs1048943, GSTM1, mEH-rs1051740, XRCC1-rs1799782 and XRCC1-rs25489 and the methylations of p16 and RASSF1A gene, and the length of telomere in the peripheral blood from 200 lung cancer patients and 200 health persons, the discriminative model was established through decision tree and ANN technique.

Results

ACU of the discriminative model based on multiple tumour markers increased by about 10%; The accuracy rate of decision tree model and ANN model for testing set were 93.00% and 89.62% respectively. The ROC analysis showed the decision tree model's AUC is 0.929 (0.894∼0.964), the ANN mode

SUBMITTER: Wang Z 

PROVIDER: S-EPMC5706913 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature