Breast Cancer Prediction Based on Machine Learning

作     者：Yuanzhou Wei Dan Zhang Meiyan Gao Yuanhao Tian Ya He Bolin Huang Changyang Zheng Yuanzhou Wei;Dan Zhang;Meiyan Gao;Yuanhao Tian;Ya He;Bolin Huang;Changyang Zheng

作者机构：College of Engineering and Computing Florida International University Miami Florida USA School of Information Science and Engineering Shandong University Jinan China Steven J. Green School of International & Public Affairs Florida International University Miami Florida USA School of Economics Capital University of Economics and Business Beijing China Viterbi School of Engineering University of Southern California Los Angeles California USA School of Engineering Brown University Providence Rhode Island USA 

出 版 物：《Journal of Software Engineering and Applications》 (软件工程与应用（英文）)

年 卷 期：2023年第16卷第8期

页      面：348-360页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Logistic Regression Decision Tree Random Forest Prediction 

摘      要：Breast cancer is a significant health concern, necessitating accurate prediction models for early detection and improved patient outcomes. This study presents a comparative analysis of three machine learning models, namely, Logistic Regression, Decision Tree, and Random Forest, for breast cancer prediction using the Wisconsin breast cancer diagnostic dataset. The dataset comprises features computed from fine needle aspirate images of breast masses, with 357 benign and 212 malignant cases. The research findings highlight that the Random Forest model, leveraging the top 5 predictors—“concave points_mean, “area_mean, “radius_mean, “perimeter_mean, and “concavity_mean, achieves the highest predictive accuracy of approximately 95% and a cross-validation score of approximately 93% for the test dataset. These results demonstrate the potential of machine learning approaches in breast cancer prediction, underscoring their importance in aiding early detection and diagnosis.