Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

作     者：Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier 

作者机构：School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China 

出 版 物：《IAENG International Journal of Computer Science》 (IAENG Int. J. Comput. Sci.)

年 卷 期：2025年第52卷第1期

页      面：1-10页

核心收录：

基　　金：This study was supported by the Anhui Provincial Quality Engineering Project \u201DConstruction and Practice of New Computer Science and Technology Specialities under the Background of Industry-Teaching Integration\u201D (2023sx154) and the Anhui Province New Era Education Quality Project \u201DApplication of Double Weighted Random Forest Multi-Label Algorithm in Predicting the direction of breast cancer Metastasis\u201D (2022xscx090). It was also supported by the Natural Science Research Project of Universities in Anhui Province in 2017 (key) \u201DEndocrine personalized therapy for breast cancer patients based on Data Mining technology\u201D (KJ2017A390) 

主　　题：Lung cancer 

摘      要：Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.