Development of diagnostic model of lung cancer based on multiple tumor markers and data mining
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Zhaoxian Wang1,*, Feifei Feng1,*, Xiaoshan Zhou1,2,*, Liju Duan1, Jing Wang3, Yongjun Wu1 and Na Wang1
1College of Public Health, Zhengzhou University, Henan, China
2Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Karolinska University Hospital, Huddinge, Sweden
3The First Affiliated Hospital of Zhengzhou University, Henan, China
*These authors have contributed equally to this work
Na Wang, email: email@example.com
Yongjun Wu, email: firstname.lastname@example.org
Keywords: lung cancer, decision tree, ANN, diagnostic model, tumor marker
Received: February 10, 2017 Accepted: August 26, 2017 Published: October 19, 2017
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.
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