Statistical analyses
cancer, hospital‐based, breast cancer
REGRESSION, CANCER, BREAST CANCER
In this hospital‐based case–control study, participants were split into a training set and a test set at random in a 7:3 ratio, with 2356 participants in the training set and 1010 participants in the test set. The ideal model was created using the training set, and its performance was assessed using the test set. The distinctions between cases and controls in terms of general demographic and characteristic were assessed using the t‐test for continuous variables or In order to enhance the model's performance, we also created machine learning models, namely the LASSO regression model and support vector machine (SVM), using gglassoFor the evaluation of the developed models, we used the test dataset. We estimated the breast cancer risk score using the CKB model, Gail model, and Tyrer–Cuzick model to compare their discrimination with the models developed in our study. The 5‐year CKB risk score was calculated based on the breast cancer risk prediction model developed using data from the China Kadoorie Biobank covering ten areas across China. This model takes into account factors like age, height, BMI, education, place of residence, age at menarche, parity, and family history of cancer.All the analyses were performed in R studio version 4.1.0. Statistical significance was determined using the threshold 