Abstract
Breast cancer is one of the most common cancer diseases in women. The rapid and accurate diagnosis of breast cancer is of great significance for the treatment of cancer. Artificial intelligence and machine learning algorithms are used to identify breast malignant tumors, which can effectively solve the problems of insufficient recognition accuracy and long time-consuming in traditional breast cancer diagnosis methods. To solve these problems, we proposed a method of attribute selection and feature extraction based on random forest (RF) combined with principal component analysis (PCA) for rapid and accurate diagnosis of breast cancer. Firstly, RF was used to reduce 30 attributes of breast cancer categorical data. According to the average importance of attributes and out of bag error, 21 relatively important attribute data were selected for feature extraction based on PCA. The seven features extracted from PCA were used to establish an extreme learning machine (ELM) classification model with different activation functions. By comparing the classification accuracy and training time of these different models, the activation function of the hidden layer was determined as the sigmoid function. When the number of neurons in the hidden layer was 27, the accuracy of the test set was 98.75%, the accuracy of the training set was 99.06%, and the training time was only 0.0022 s. Finally, in order to verify the superiority of this method in breast cancer diagnosis, we compared with the ELM model based on the original breast cancer data and other intelligent classification algorithm models. The algorithm used in this article has a faster recognition time and a higher recognition accuracy than other algorithms. We also used the breast cancer data of breast tissue reactance features to verify the reliability of this method, and ideal results were obtained. The experimental results show that RF-PCA combined with ELM can significantly reduce the time required for the diagnosis of breast cancer, which has the ability of rapid and accurate identification of breast cancer and provides a theoretical basis for the intelligent diagnosis of breast cancer.
Highlights
Cancer is a disease that seriously threatens human health
We put forward a new solution based on attribute selection and feature extraction for rapid diagnosis of breast cancer, which is called random forest (RF)-principal component analysis (PCA)
We used the attribute selection based on RF of algorithm to select the useful attributes of quantitative feature data of breast tumor cell images and used the feature extraction algorithm based on PCA to reduce the dimension of data after attribute selection
Summary
Cancer is a disease that seriously threatens human health. The latest annual report on cancer incidence in the United States (Siegel et al, 2020) shows that it is estimated that in 2020, 1,806,590 new cancer cases will be found in the United States, which is equivalent to nearly 5,000 people suffering from cancer every day. There will be 606,520 cancer deaths, which is equivalent to more than 1,600 cancer deaths per day. Over the most recent 5−year period (2012–2016), the breast cancer incidence rate increased slightly by 0.3% per year (DeSantis et al, 2019). More and more researchers are committed to the research of cancer diagnosis and treatment methods (Gebauer et al, 2018). Detection and diagnosis of breast cancer are very helpful for treatment. If breast cancer is detected early, it can guide clinically targeted prevention and treatment measures, reduce the recurrence rate of breast cancer, improve the prognosis of patients, and prolong the life cycle of patients (Charaghvandi et al, 2017). How to quickly and accurately predict breast malignant tumors has become the key to the breast cancer diagnosis
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