Abstract:

In order to assist the medical staff to diagnose breast cancer more effectively by palpation imaging technology, intelligent diagnosis model and method of palpation imaging breast cancer were established. Based on clinical data for early breast cancer screening and risk assessment, machine learning algorithms of decision tree, neural network, SVM, logistic regression, Bayesian network and five voting methods were adopted to distinguish breast tumor, or positive and negative outcome in algorithms. The positive sample data was incremented by the SMOTE algorithm, intelligent diagnosis model was established, and model can automatically diagnose breast tumors. Palpation imaging intelligent diagnosis model of breast cancer correctly screens all cases of breast cancer confirmed by pathology, and the accuracy of the model is as high as 98%. The intelligent diagnosis model is excellent as a screening modality for the detection of breast cancer.

Key words: intelligent diagnosis, clinical data, machine learning, SMOTE algorithm