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

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.

Automated summary

Breast cancer is the second leading cause of death in developed and developing nations, characterized by gene mutation, constant pain, size fluctuations, color, and breast skin texture. By applying various classification algorithms on the WBCD dataset, it was found that random forest achieved a classification accuracy of 96.24%, outperforming other classifiers.