Association between frailty and breast cancer incidence and construction of a breast cancer prediction model based on machine learning.

Objective: The relationship between frailty and breast cancer (BC) has been scarcely investigated. This study aims to explore the association between frailty and BC and to develop a predictive model for BC risk based on the frailty index (FI). Methods: This study utilized data from subjects with recorded frailty assessments and BC diagnosis collected by the National Health and Nutrition Examination Survey between 2003 and 2020. FI was assessed by 49 frailty indicators. Frailty was defined as FI > 0.20. First, logistic regression was used to analyze the link between frailty and BC. Subsequently, the dataset was divided into training and validation sets in a 7:3 ratio. Based on the training set, LASSO regression and 10-fold cross-validation were used to screen for the variables with the greatest predictive value. Then, the study built and evaluated eight machine learning (ML) models. Finally, SHapley Additive explanations was applied to interpret the best-performing model. Results: After data were excluded, a total of 4,473 patients were ultimately included. Logistic analysis revealed a positive correlation between FI and BC prevalence ( Conclusion: FI is positively correlated with the prevalence of BC. BC prediction model based on the attenuation index that this study developed and validated has high sensitivity, aids in early BC identification in frail individuals, and suggests that future studies refine its applicability.