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Risk prediction of esophageal cancer using SOM clustering, SVM and GA-SVM. (English) Zbl 07240055
Pan, Linqiang (ed.) et al., Bio-inspired computing: theories and applications. 14th international conference, BIC-TA 2019, Zhengzhou, China, November 22–25, 2019. Revised selected papers. Part II. Singapore: Springer (ISBN 978-981-15-3414-0/pbk; 978-981-15-3415-7/ebook). Communications in Computer and Information Science 1160, 345-358 (2020).
Summary: In order to find the blood indicators significantly associated with the survival of esophageal cancer and predict the classification of patients’ risk levels in an affordable, convenient, and accurate manner, a method based on self-organizing maps (SOM) neural network clustering and support vector machine prediction risk levels is proposed. Seventeen blood indicators of 501 esophageal cancer patients are pretreated. Nine factors related to patient survival are found by using SOM clustering method, and verified by using COX multi-factor risk regression model. Two critical thresholds for survival are found by plotting the ROC curve twice, and the lifetime are divided into three risk levels. The following is to select the data information of 9 blood indicators of 180 patients, including risk level 1, risk level 2, and risk level 3. Using the SVM method, patients’ risk levels are predicted, the accuracy rate reached 91.11%. After the parameters optimization of genetic algorithm (GA), the accuracy rate reached 93.33%. Compared with BP neural network, it is concluded that SVM is superior to BP neural networks algorithm, and GA-SVM is better than SVM. This article provides a new method for early diagnosis and prediction of esophageal cancer.
For the entire collection see [Zbl 1440.68010].
MSC:
68Q07 Biologically inspired models of computation (DNA computing, membrane computing, etc.)
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