Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Colorectal Cancer Survivors

doi:10.3390/app10041355

CSMUIR > College of Health Care and Management > Department of Medical Informatics (including MS Program) > Journal paper > Item 310902500/21055

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/21055

Title:	Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Colorectal Cancer Survivors
Authors:	Wen-Chien Ting;Horng-Rong Chang;Chi-Chang Chang;Chi-Jie Lu
Contributors:	應用資訊科學學系暨碩士班
Keywords:	risk factors;second primary cancer (SPC), colorectal cancer;machine learning;classification techniques;extreme gradient boosting
Date:	2020-02-17
Issue Date:	2020-08-10T08:46:35Z (UTC)
Publisher:	Appl. Sci.
Abstract:	Abstract: Colorectal cancer is ranked third and fourth in terms of mortality and cancer incidence in the world. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five machine learning classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. A total of 4287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.
URI:	https://ir.csmu.edu.tw:8080/ir/handle/310902500/21055
Relation:	Appl. Sci. 2020, 10(4), 1355
Appears in Collections:	[ Department of Medical Informatics (including MS Program) ] Journal paper

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