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http://dx.doi.org/10.30693/SMJ.2020.9.4.126

Accessing the Clustering of TNM Stages on Survival Analysis of Lung Cancer Patient  

Choi, Chulwoong (전남대학교 인공지능융합학과)
Kim, Kyungbaek (전남대학교 인공지능융합학과)
Publication Information
Smart Media Journal / v.9, no.4, 2020 , pp. 126-133 More about this Journal
Abstract
The treatment policy and prognosis are determined based on the final stage of lung cancer patients. The final stage of lung cancer patients is determined based on the T, N, and M stage classification table provided by the American Cancer Society (AJCC). However, the final stage of AJCC has limitations in its use for various fields such as patient treatment, prognosis and survival days prediction. In this paper, clustering algorithm which is one of non-supervised learning algorithms was assessed in order to check whether using only T, N, M stages with a data science method is effective for classifying the group of patients in the aspect of survival days. The final stage groups and T, N, M stage clustering groups of lung cancer patients were compared by using the cox proportional hazard model. It is confirmed that the accuracy of prediction of survival days with only T, N, M stages becomes higher than the accuracy with the final stages of patients. Especially, the accuracy of prediction of survival days with clustering of T, N, M stages improves when more or less clusters are analyzed than the seven clusters which is same to the number of final stage of AJCC.
Keywords
Lung Cancer; Clustering; TNM Stage; Survival Analysis; Kaplan-Meier Survival Curve;
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Times Cited By KSCI : 6  (Citation Analysis)
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