Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58604
Title: Recurrence impact of primary site and pathologic stage in patients diagnosed with colorectal cancer
Authors: Wen Chien Ting
Yen Chiao Angel Lu
Chi Jie Lu
Chalong Cheewakriangkrai
Chi Chang Chang
Keywords: Decision Sciences
Engineering
Issue Date: 1-Jan-2018
Abstract: © 2018, Chinese Society for Quality. All rights reserved. Detection of cancer recurrence for events of asymptomatic is highly related to the survival. In this study, we considered the variable screening mechanisms and four data mining techniques. The pathological data were obtained from Cancer Center of Chung Shan Medical University Hospital. Results show that primary site and pathologic stage are important independent risk factors. Before variable screenings showed that the highest of average accuracy and area under the curve (AUC) were: C5.0. Screening results of the colon site, the accuracy of < IIb stage was the highest with support vector machine (SVM) (0.91), and that of ≥ IIb stage was the highest with extreme learning machine (ELM) (0.86). In the rectum site, the accuracy of < IIb stage was the best with ELM (0.96), and that of ≥ IIb stage was the highest with multivariate adaptive regression splines (MARS) (0.89) and ELM (0.89). The results of this study provide that for recurrence detection in colorectal cancer patients can be used by clinicians to recommend adjuvant treatment.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051539067&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58604
ISSN: 10220690
Appears in Collections:CMUL: Journal Articles

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