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Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approach
Sirorat Pattanapairoj1, Atit Silsirivanit2, Kanha Muisuk3
1Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand; System Modeling for Industry Research Group, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand.
Objective:
Cholangiocarcinoma (CCA) is usually fatal because of the absence of tests for early detection and lack of effective therapy. Tumor markers with adequate diagnostic values are of clinical significance. This study is aimed to improve the diagnostic power of serum markers using the computational data mining technique to develop a combined diagnostic model that yielded the best diagnostic values for CCA.
Design And Methods:
Eight CCA-associated markers-carcinoembryonic antigen, carbohydrate antigen 19-9, alkaline phosphatase (ALP), and gamma glutamyl transferase, biliary-ALP, mucin5AC, CCA-associated carbohydrate antigen (CCA-CA) and CA-S27-were used as the inputs for the C4.5 decision tree classification model and the selected model was confirmed by ANN analyses. Eight serum markers for CCA were determined in the training set of 85 histologically proven-CCA patients and 82 control subjects. The chosen set of combined markers that gave the best diagnostic values for CCA was then validated in the testing set of 22 CCA patients and 60 controls.
Results:
A decision tree diagram built by the C4.5 algorithm suggested the serial analysis of CCA-CA and ALP for distinguishing CCA patients from non-CCA subjects with all diagnostic parameters ≥95%. The combined tests showed a precise diagnosis in the testing set.
Conclusions:
The C4.5 model indicates the combined markers of CCA-CA and ALP that produced the more precise diagnosis for CCA.
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