[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117430-en":3,"doc-seo-117430-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117430,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","A Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions","This study develops a machine learning decision-support approach to predict breast cancer recurrence risk and assist personalized treatment planning. Clinical records from 1,131 breast cancer patients were preprocessed and standardized, then LASSO feature selection identified six key recurrence predictors. An explainable ensemble learning model was trained using multiple algorithms, with SHAP providing transparent, feature-level attribution to recurrence risk. The ensemble achieved stronger predictive performance (AUC 0.817) than the best single model (AUC 0.711), improving both accuracy and interpretability.","Cancer Management and Research  \nCancer Management and Research downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \n Open Access Full Text Article ORIGINAL RESEARCH  \nA Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions Through Comprehensive Feature Selection and Explainable Ensemble Learning  \nTsair-Fwu Lee 1–4 , Jun-Ping Shiau 1 , 5 , Chia-Hui Chen 1 , Wen-Ping Yun 1 , Cheng-Shie Wuu 6 , Yu-Jie Huang 7 , Shyh-An Yeh 1 , 8 , 9 , Hui-Chun Chen 7 , Pei-Ju Chao 1 , 7  \n1Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan, Republic of China; 2Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, Republic of China; 3Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan, Republic of China; 4School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan, Republic of China; 5Division of Breast Oncology and Surgery, Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung, 807, Taiwan, Republic of China; 6Department of Radiation Oncology, Columbia University, New York, NY, USA; 7Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan, Republic of China; 8Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung, 82445, Taiwan, Republic of China; 9Department of Radiation Oncology, E-DA Hospital, Kaohsiung, 82445, Taiwan, Republic of China  \nCorrespondence: Pei-Ju Chao; Hui-Chun Chen, Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan, Republic of China, Tel +886-7-731-7123 ext 7060, Fax +886-7-732-2813, [Email pjchao99@gmail.com](Email pjchao99@gmail.com); [kuas999@gmail.com](kuas999@gmail.com)  \nPurpose: This study investigates the efficiency of a machine learning model integrating least absolute shrinkage and selection operator (LASSO) feature selection with ensemble learning in predicting recurrence risk and supporting personalized treatment decisions in breast cancer patients.  \nMaterials and Methods: Clinical data from 1,131 breast cancer patients (1,056 nonrecurrent and 75 recurrent) were collected from Kaohsiung Medical University Hospital’s electronic health record system. After preprocessing and standardization, LASSO was applied for feature selection. An ensemble learning model was developed based on multiple machine learning algorithms, with SHAP (Shapley additive explanations) used for interpretability.  \nResults: The ensemble model achieved an AUC of 0.817, outperforming the best single model (AUC 0.711), demonstrating improved predictive accuracy and stability. LASSO identified six key predictors: regional lymph node positivity, ER status, Ki-67, lymphovascular invasion, tumor size, and age at diagnosis. SHAP analysis enhanced transparency by quantifying the contribution of each feature to recurrence risk, improving clinical understanding.  \nConclusion: This LASSO-enhanced ensemble model significantly improves the accuracy and interpretability of breast cancer recurrence prediction. By identifying individualized recurrence risks through SHAP analysis, the model supports more precise, datadriven clinical decision-making. These findings demonstrate its potential as a clinical decision support tool for guiding personalized treatment strategies, contributing to more effective breast cancer management.  \nPlain language summary: Breast cancer is the most common cancer in women worldwide, and despite treatment, some patients experience recurrence, meaning the cancer returns after initial therapy. Identifying which patients are at higher risk of recurrence is crucial for personalized ","cbCaiusObQZxX6C6","https://ap.wps.com/l/cbCaiusObQZxX6C6","pdf",2650915,1,16,"English","en",105,"# Purpose\n# Materials and Methods\n## Data source and preprocessing\n## Feature selection and ensemble model\n## Explainability with SHAP\n# Results\n# Conclusion\n# Plain language summary\n# Publication details","[{\"question\":\"What problem does the study address in breast cancer care?\",\"answer\":\"The study targets predicting breast cancer recurrence risk and supporting personalized treatment decisions using patient data and explainable machine learning.\"},{\"question\":\"How does the model select predictive features?\",\"answer\":\"LASSO feature selection is applied after preprocessing and standardization to identify key predictors of recurrence.\"},{\"question\":\"How is the model made interpretable for clinical understanding?\",\"answer\":\"SHAP (Shapley additive explanations) quantifies each feature’s contribution to recurrence risk, improving transparency for clinicians.\"}]","A Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions | 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