Modeling study of breast cancer diagnosis based on tGSSA-SVM

Yue Zhang

doi:10.1117/12.2674839

28 April 2023 Modeling study of breast cancer diagnosis based on tGSSA-SVM

Yue Zhang

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126262E (2023) https://doi.org/10.1117/12.2674839
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

In order to further improve the accuracy and generalization performance of traditional Support Vector Machine (SVM) model and improve the problems of low accuracy and poor generalization of the constructed classifier, the penalty parameter C and the kernel parameter of the support vector machine (SVM) are improved using the sparrow search algorithm (t-GSSA) based on adaptive t distribution with golden sine improvement. The tGSSA-SVM breast cancer identification model was developed by performing the optimization search. The Wisconsin breast cancer data set (WBSCD) was applied for experiments and compared with the traditional SVM, PSO-SVM, and WOA-SVM. The results showed that the performance of the optimized tGSSA-SVM diagnostic model was improved compared with the existing methods in terms of accuracy, etc., and it was a better modeling algorithm.

Citation Download Citation

Yue Zhang "Modeling study of breast cancer diagnosis based on tGSSA-SVM", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126262E (28 April 2023); https://doi.org/10.1117/12.2674839

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