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

// Zhaoxian Wang 1, * , Feifei Feng 1, * , Xiaoshan Zhou 1, 2, * , Liju Duan 1 , Jing Wang 3 , Yongjun Wu 1 and Na Wang 1 1 College of Public Health, Zhengzhou University, Henan, China 2 Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Karolinska University Hospital, Huddinge, Sweden 3 The First Affiliated Hospital of Zhengzhou University, Henan, China * These authors have contributed equally to this work Correspondence to: Na Wang, email: wfengqiao@zzu.edu.cn Yongjun Wu, email: wuyongjun@zzu.edu.cn Keywords: lung cancer, decision tree, ANN, diagnostic model, tumor marker Received: February 10, 2017      Accepted: August 26, 2017      Published: October 19, 2017 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 model’s AUC is 0.894 (0.853~0.935). However, the classify accuracy rate and AUC of Fisher discriminatory analysis model are all about 0.7. Conclusion: The early intelligent discriminative model of lung cancer based on multiple tumor markers and data mining techniques has a higher accuracy rate and might be useful for early diagnosis of lung cancer.