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Study on Breast Tumor Classification based on SP-SVM Model

The increase in the sample size and characteristics of the medical data acquired by the healthcare industry has placed greater demand on the methods used to process these data. As a classical paradigm in machine learning, support vector machine (SVM) can handle small samples and nonlinear problems well, but their classification prediction performance in dealing with multi-sample and multi-featured indicator datasets do not meet the demand. Therefore, this paper applies Stacking method to integrate SVM as a way to improve the classification prediction performance of the model. Based on this, in order to solve the problem of information redundancy that may be brought by multiple feature indicators, we introduced principal component analysis (PCA) to reduce the dimensionality of the dataset as a way to improve the efficiency and performance of model classification prediction. Finally, after empirical analysis we conclude that the SP-SVM model is able to predict the classification of multiple samples and multi-featured indicator datasets well.