Abstract
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. The original datasets are 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, while the other type is used as the validation sets to test the correlation indicators and model. Thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by Kohonen algorithm in the development sets. Ten different machine learning classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. The artificial bee colony-support vector machine (ABC-SVM) has better prediction accuracy than the other nine algorithms and has a shorter training time. The average accuracy of the 10-fold cross-validation reached 81.5\(\%\) by ABC-SVM algorithm. In the development sets, a model with the great merit for the degree of differentiation is found based on logistic regression algorithm. The AUC value of the model is 0.672 and 0.753 in the development sets and validation sets, respectively. \(p-values\) are less than 0.05. The results are shown that the model has a high predictive value for the differentiation of esophageal squamous cell carcinoma.
This work was supported in part by the National Key Research and Development Program of China for International S and T Cooperation Projects under Grant 2017YFE0103900, in part by the Joint Funds of the National Natural Science Foundation of China under Grant U1804262, in part by the State Key Program of National Natural Science of China under Grant 61632002, in part by the Foundation of Young Key Teachers from University of Henan Province under Grant 2018GGJS092, in part by the Youth Talent Lifting Project of Henan Province under Grant 2018HYTP016, in part by the Henan Province University Science and Technology Innovation Talent Support Plan under Grant 20HASTIT027, in part by the Zhongyuan Thousand Talents Program under Grant 204200510003, and in part by the Open Fund of State Key Laboratory of Esophageal Cancer Prevention and Treatment under Grant K2020-0010 and Grant K2020-0011.
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Yang, Y., Ji, H., Sun, J., Wang, Y. (2021). The Predictive Model of Esophageal Squamous Cell Carcinoma Differentiation. In: Pan, L., Pang, S., Song, T., Gong, F. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2020. Communications in Computer and Information Science, vol 1363. Springer, Singapore. https://doi.org/10.1007/978-981-16-1354-8_22
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