Breast Cancer Prediction using Supervised Machine Learning Techniques
Sonal Singh, Jatin Prasad, Shivangini Prasad, Srinivas Naik · 2021 IEEE 18th India Council International Conference (INDICON) · 2021
Breast cancer is a form of tumor that develops in the breast tissues. It is the most frequent cancer in women worldwide, and it is one of the leading causes of mortality in women. Therefore, early detection and diagnosis will benefit patients who have breast cancer. This paper has compared nine supervised machine learning techniques named Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), Logistic Regression (LR), Gaussian Naive Bayes (GNB), Artificial Neural Network (ANN), XGBoost Classifier (XGB) and AdaBoost Classifier (ADB) in order to classify the tumors into benign and malignant. The data set is from the UCI Machine Learning repository, which is used for predicting breast cancer. The performance of models is measured with respect to the accuracy, precision, recall, and F1 score. The results reveal that the ANN obtained the highest accuracy, precision, and F1 score of 0.993, 0.998, and 0.991, respectively.