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 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.