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International Conference on Bio-Inspired Computing: Theories and Applications

BIC-TA 2019: Bio-inspired Computing: Theories and Applications pp 345–358Cite as

Risk Prediction of Esophageal Cancer Using SOM Clustering, SVM and GA-SVM

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1160))

Abstract

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.

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Acknowledgements

This work was supported in part by the National Key R and D Program of China for International S and T Cooperation Projects (2017YFE0103900), in part by the Joint Funds of the National Natural Science Foundation of China (U1804262), in part by the State Key Program of National Natural Science of China under Grant 61632002, in part by the National Natural Science of China under Grant 61603348, Grant 61775198, Grant 61603347, and Grant 61572446, in part by the Foundation of Young Key Teachers from University of Henan Province (2018GGJS092), and in part by the Youth Talent Lifting Project of Henan Province (2018HYTP016) and Henan Province University Science and Technology Innovation Talent Support Plan under Grant 20HASTIT027.

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Authors and Affiliations

  1. Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Yuli Yang, Zhi Li & Yanfeng Wang

  2. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Yuli Yang, Zhi Li & Yanfeng Wang

Authors
  1. Yuli Yang
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  2. Zhi Li
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  3. Yanfeng Wang
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Corresponding author

Correspondence to Yanfeng Wang .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Yang, Y., Li, Z., Wang, Y. (2020). Risk Prediction of Esophageal Cancer Using SOM Clustering, SVM and GA-SVM. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_29

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  • DOI: https://doi.org/10.1007/978-981-15-3415-7_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3414-0

  • Online ISBN: 978-981-15-3415-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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