Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification

Abstract

Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.

Classification Prediction of Breast Cancer Based on Machine Learning

“…(2) Performance comparison of each model in this paper when the data set is divided into a training set and test set in 7 : 3. (3) Comparison with some models in the literature [11][12][13][14].…”

“…In medicine, it is very important for a disease to be diagnosed. [11][12][13][14], and the specifc results are shown in Table 8.…”

“…Te recall and accuracy of the model in the literature [13] are 0.99 and 0.98, respectively, and the recall and accuracy of the model in this paper are 1.00 and 0.974, respectively, compared with the literature [13], the recall of the model is high, and the recall in medical diagnosis indicates the probability of detecting malignant cancer cells, which is of great signifcance for the classifcation of breast cancer cells, so the XGBoost model established in this paper has a better prediction efect and can be used as a medical tool to assist doctors to make treatment plans for breast cancer patients. Reference Model Recall Accuracy Literature [11] XGBoost 0.958 0.979 Literature [12] XGBoost 0.970 0.960 Literature [13] Logistic regression 0.99 0.98 Literature [14] Random forest 0.943 0.962 Te model of this paper XGBoost 1.00 0.974…”

“…(2) Performance comparison of each model in this paper when the data set is divided into a training set and test set in 7 : 3. (3) Comparison with some models in the literature [ 11 – 14 ]. When the data set is divided into a training set and test set by 8 : 2, the performance of each model is shown in Table 6 .…”

“…Monirujjaman Khan et al [ 13 ] used multiple machine learning models to identify breast cancer, and random forest, decision tree, K-nearest neighbor, and logistic regression were the algorithms with higher F 1-score, 96%, 95%, 90%, and 98%, respectively. Bhardwaj et al [ 14 ] used multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic algorithm (GP), and random forest (RF) to classify benign and malignant breast cancer cells, and the experimental results showed that the optimal classifier was RF with a classification accuracy of 96.24%. Dong and Ma [ 15 ] studied the possible markers of triple-negative breast cancer, and machine learning algorithms were used to predict whether people had triple-negative breast cancer.…”