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

doi:10.3390/app10041355

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24437

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/24437

Title:	Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Colorectal Cancer Survivors
Authors:	Ting, WC;Chang, HR;Chang, CC;Lu, CJ
Keywords:	risk factors;second primary cancer (SPC);colorectal cancer;machine learning;classification techniques;extreme gradient boosting
Date:	2020
Issue Date:	2022-08-09T08:02:18Z (UTC)
Publisher:	MDPI
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:	http://dx.doi.org/10.3390/app10041355 https://www.webofscience.com/wos/woscc/full-record/WOS:000525287900162 https://ir.csmu.edu.tw:8080/handle/310902500/24437
Relation:	APPLIED SCIENCES-BASEL ,2020 ,v10 ,issue 4
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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