Breast Cancer Prediction Using Machine Learning Algorithms Based on Wisconsin Breast Cancer Dataset

Abstract

Cancer is deadly disease which is caused due to uncontrolled growth of the cells and forms from the extra mass tissue known as tumor. There are over 200 types of cancer. Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all Cancers. Breast cancer is the second leading cause of cancer death in women. The chance that a woman will die from breast cancer is about 1 in 39 (about 2.6%) [1]. In 2020, there were 2.3 million women diagnosed with breast cancer and 685 000 deaths globally [2]. When detected in its early stages, there is a 30% chance that the cancer can be treated effectively, but the late detection of advanced-stage tumors makes the treatment more difficult [3,4]. By using Machine Learning we have built a model which can predict the possibility of having breast cancer. The model that we have built was trained by Wisconsin Breast Cancer dataset (WDBC) for breast cancer diagnosis prediction. On experiment, these data were processed and analyzed by various data pre-processing techniques. Then Some classic Machine Learning algorithms like Naive Bayes, Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree and Neural Network were used for building the model and the performance of each of them was measured using metrics like prediction accuracy on the testing and training data, Precision, Recall, F1 score and Support. Overall Support Vector Machine (SVM) performed better than others. So, the Support Vector Machine model was chosen for the prediction of the disease.