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

Yang, Yan; Li, Zhi; Wang, Yanfeng

doi:10.1007/978-981-15-3415-7_29

Cited by 1 publication

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“…In order to reduce the distance between the winning neuron and the input vector, the connection weights of neighboring neurons around the winning neuron are adjusted to be closer to the original input vector. Eventually different categories are gradually formed (Sun et al, 2020;Yang et al, 2020). As shown in Figure 1, a Kohonen neural network of 36-21 structure is established.…”

Section: Clustering Of Data Setsmentioning

confidence: 99%

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

et al. 2020

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The diagnosis of the degree of differentiation of tumor cells can help physicians to make timely detection and take appropriate treatment for the patient's condition. In this study, the original dataset is clustered into two independent types by the Kohonen clustering algorithm. One type is used as the development sets to find correlation indicators and establish predictive models of differentiation, while the other type is used as the validation sets to test the correlation indicators and models. In the development sets, thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by the Kohonen clustering algorithm. Thirteen relevant indicators are used as input features and the degree of tumor differentiations is used as output. Ten classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Artificial bee colony-support vector machine (ABC-SVM) predicts better than the other nine algorithms, with an average accuracy of 81.5% for the 10-fold cross-validation. Based on logistic regression and ReliefF algorithm, five models with the greater merit for the degree of differentiation are found in the development sets. The AUC values of the five models are 0.672, 0.628, 0.630, 0.628, and 0.608 (P < 0.05). The AUC values of the five models in the validation sets are 0.753, 0.728, 0.744, 0.776, and 0.868 (P < 0.0001). The predicted values of the five models are constructed as the input features of ABC-SVM. The accuracy of the 10-fold cross-validation reached 82.0 and 86.5% in the development sets and the validation sets, respectively.

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Section: Clustering Of Data Setsmentioning

confidence: 99%