A machine learning approach to predict self-efficacy in breast cancer survivors
Abstract Purpose 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 f...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-03155-9 |
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| author | İsmail Toygar Su Özgür Gülcan Bağçivan Ezgi Karaçam Hilal Benzer Ferda Akyüz Özdemir Halise Taşkın Duman Özlem Ovayolu |
| author_facet | İsmail Toygar Su Özgür Gülcan Bağçivan Ezgi Karaçam Hilal Benzer Ferda Akyüz Özdemir Halise Taşkın Duman Özlem Ovayolu |
| author_sort | İsmail Toygar |
| collection | DOAJ |
| description | Abstract Purpose 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). Results 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 of the 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) model. 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 professionals in enhancing the care provided to breast cancer survivors. It is of the 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. |
| format | Article |
| id | doaj-art-6d1c125be63f4d1496a8bd92b72ec9e7 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-6d1c125be63f4d1496a8bd92b72ec9e72025-08-24T11:33:37ZengBMCBMC Medical Informatics and Decision Making1472-69472025-08-0125111210.1186/s12911-025-03155-9A machine learning approach to predict self-efficacy in breast cancer survivorsİsmail Toygar0Su Özgür1Gülcan Bağçivan2Ezgi Karaçam3Hilal Benzer4Ferda Akyüz Özdemir5Halise Taşkın Duman6Özlem Ovayolu7Fethiye Faculty of Health Sciences, Muğla Sıtkı Koçman UniversityEge University Faculty of Medicine, EgeSAM-Translational Pulmonary Research CenterKoç University Faculty of NursingDr Sadi Konuk Training and Research HospitalHasan Kalyoncu University Vocational SchoolFethiye Faculty of Health Sciences, Muğla Sıtkı Koçman UniversityFethiye Faculty of Health Sciences, Muğla Sıtkı Koçman UniversityGaziantep University Faculty of Health SciencesAbstract Purpose 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). Results 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 of the 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) model. 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 professionals in enhancing the care provided to breast cancer survivors. It is of the 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.https://doi.org/10.1186/s12911-025-03155-9Breast cancerSurvivorshipSelf-efficacyMachine learning |
| spellingShingle | İsmail Toygar Su Özgür Gülcan Bağçivan Ezgi Karaçam Hilal Benzer Ferda Akyüz Özdemir Halise Taşkın Duman Özlem Ovayolu A machine learning approach to predict self-efficacy in breast cancer survivors BMC Medical Informatics and Decision Making Breast cancer Survivorship Self-efficacy Machine learning |
| title | A machine learning approach to predict self-efficacy in breast cancer survivors |
| title_full | A machine learning approach to predict self-efficacy in breast cancer survivors |
| title_fullStr | A machine learning approach to predict self-efficacy in breast cancer survivors |
| title_full_unstemmed | A machine learning approach to predict self-efficacy in breast cancer survivors |
| title_short | A machine learning approach to predict self-efficacy in breast cancer survivors |
| title_sort | machine learning approach to predict self efficacy in breast cancer survivors |
| topic | Breast cancer Survivorship Self-efficacy Machine learning |
| url | https://doi.org/10.1186/s12911-025-03155-9 |
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