Comparison of Machine Learning Classifiers for Breast Cancer Diagnosis

Cancer is a fatal disease and it has several types in the world. Among all the types of cancer, Breast Cancer is a frequent type of cancer in women. In 2020, there were 2.3 million women diagnosed with breast cancer and 6,85000 deaths globally. Therefore, early prediction of breast cancer is important for proper treatment and healthy life. Machine learning approaches contribute a lot to cancer disease diagnosis as well as other fatal diseases. In this paper, Breast Cancer Wisconsin dataset is considered with different machine learning techniques of cancer classification. Techniques are evaluating with evaluation matrices mainly included Accuracy, Precision, Recall, F1 Score, and AUC are considered to evaluate the robustness of the machine learning model. We have compared different machine learning methods with each other on the behalf of evaluation metrics and we have achieved 96.5% accuracy with the 'Gradient Boosted Tree' Machine learning classifier.