[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126455-en":3,"doc-seo-126455-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126455,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",7,"Healthcare","A machine learning approach to predict self-efficacy in breast cancer survivors - Predictors and vulnerable groups","Purpose: determine predictors of self-efficacy in breast cancer survivors and identify vulnerable groups. Methods: a descriptive study (Nov 2023–Apr 2024) across three hospitals in Türkiye enrolled 430 breast cancer survivors and collected data via face-to-face surveys using a patient identification form and the Breast Cancer Survivor Self-Efficacy Scale. Four machine learning models (logistic regression, random forest, support vector machine, XGBoost) evaluated patient characteristics associated with higher self-efficacy. Results: education ranked highest in several models, while cancer stage and comorbidity emerged as additional important factors. Conclusion: self-efficacy is associated with sociodemographic and medical characteristics, supporting targeted healthcare interventions.","Toygar etal. BMC Medical Informatics and Decision Making (2025) 25:313  \n[https://doi.org/10.1186/s12911-025-03155-9](https://doi.org/10.1186/s12911-025-03155-9)  \nBMC Medical Informatics and Decision Making  \nRESEARCH Open Access  \nA machine learning approach to predict self- efficacy in breast cancer survivors  \nİsmail Toygar 1*†, Su Özgür2,3†, Gülcan Bağçivan4,8, Ezgi Karaçam5,9, Hilal Benzer6, Ferda Akyüz Özdemir 1, HaliseTaşkın Duman 1 and Özlem Ovayolu7  \nAbstract  \nPurpose To determine predictors of self-efficacy in breast cancer survivors and identify vulnerable groups. Methods This descriptive study was conducted between November 2023 and April 2024 at three hospitals in Türkiye and involved 430 breast cancer survivors. Data were collected through face-to-face surveys using a patient identification form and the Breast Cancer Survivor Self-Efficacy Scale. This study identified patient characteristics that indicate a tendency towards higher self-efficacy using four machine learning models; Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), XGBoost (XGB) .  \nResults The mean age of participants was 50.7 ± 11.5 years. Majority of the participants (n = 425) were female. AUC values were used as ranker for the machine learning models. The ranks ofthe models were as follows; logistic regression model (0 . 715), RF (0 . 710), SVM (0 . 704), and XGBoost (0 . 694) . Education level ranked first in the LR (0 . 3874), RF (0 . 3290), and SVM (0 . 1250) models, and was the second most important variable in the XGB (0 . 2327)  \nmodel. Conversely, the cancer stage stood out in the LR (0 . 2466) and RF (0 . 1935) models, ranking third and fourth, respectively, while it ranked third in SVM (0 . 0683) and fourth in XGB (0 . 1872) . Additionally, comorbidity ranked third in importance in the LR (0 . 2213) and RF (0 . 1681) models, but second in SVM (0 . 0705) and seventh in XGB (0 . 1393) . Conclusion The study demonstrated that the self-efficacy of breast cancer survivors was associated with their sociodemographic and medical characteristics. These characteristics may assist healthcare professional s in enhancing the care provided to breast cancer survivors. It is ofthe utmost importance to consider the aforementioned patient group as being vulnerable with regard to breast cancer survivor self-efficacy. There is a clear need for a focus on this vulnerable cohort.  \nKeywords Breast cancer, Survivorship, Self-efficacy, Machine learning  \n†İsmail Toygar and Su Özgür are joint first authors.  \n*Correspondence:  \nİsmail Toygar  \n[ismail.toygar1@gmail.com](ismail.toygar1@gmail.com)  \n1Fethiye Faculty of Health Sciences, Muğla Sıtkı Koçman University, Fethiye, Muğla 48330, Turkey  \n2Ege University Faculty of Medicine, EgeSAM-Translational Pulmonary Research Center, Bornova, İzmir 35100, Turkey  \n3Regional Hub for Cancer Registration in Northern Africa, Central and Western Asia, WHO/IARC GICR, İzmir, Turkey  \n4Koç University Faculty of Nursing, İstanbul 34010, Turkey  \n5Dr Sadi Konuk Training and Research Hospital, İstanbul 30110, Turkey 6Hasan Kalyoncu University Vocational School,  \nŞahinbey, Gaziantep 27410, Turkey  \n7Gaziantep University Faculty of Health Sciences,  \nŞahinbey, Gaziantep 27410, Turkey  \n8College of Nursing and Health Sciences, University of Massachusetts Dartmouth, North Dartmouth, MA, USA  \n9Istanbul University – Cerrahpaşa, Postgraduate Education Institute,İstanbul, Turkey  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or","cbCaismfSSoKKZ5w","https://ap.wps.com/l/cbCaismfSSoKKZ5w","pdf",1473395,10,1,12,"English","en",105,"# Abstract\n## Purpose and Methods\n## Results and Conclusion","[{\"question\":\"What is the purpose of the study on breast cancer survivorship self-efficacy?\",\"answer\":\"The study aims to identify predictors of self-efficacy in breast cancer survivors and to determine vulnerable groups.\"},{\"question\":\"How was self-efficacy measured and what data were used?\",\"answer\":\"Data were collected through face-to-face surveys using a patient identification form and the Breast Cancer Survivor Self-Efficacy Scale.\"},{\"question\":\"Which factors were most important for predicting self-efficacy?\",\"answer\":\"Education level ranked first in multiple models, while cancer stage and comorbidity also showed important associations depending on the model.\"}]","A machine learning approach to predict self-efficacy in breast cancer survivors - 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