Development and validation of a machine learning model to predict delays in seeking medical care among patients with breast cancer in China

Background Delays in seeking medical care may affect the survival rates of breast cancer patients. We aimed to explore potential risk factors for the delay in seeking medical care among breast cancer patients by constructing a highly effective machine learning (ML) prediction model. Methods A cross-sectional methodology was utilized, and the demographic and clinical characteristics of 540 patients with breast cancer in Sichuan Cancer Hospital from July 2022 to June 2023 were collected to develop a model. Feature selection was performed using a Lasso algorithm, and six ML algorithms, including XGB, LR, RF, CNB, SVM and KNN, were applied for model construction. The k-fold cross-validation method was used for internal verification. And ROC curves, calibration curves, DCA and external validation were used for model evaluation. The SHAP method was used to interpret the model visualization. Results A comprehensive analysis was conducted in a cohort of 540 patients diagnosed with breast cancer, of whom 212 patients (39.26%) experienced a delay. Lasso algorithm selected eight variables that were most suitable for model construction. The RF model demonstrated superior performance compared to the other five prediction models. The AUC values in the training set ROC, validation set ROC, and external verification ROC curves were 1.00, 0.86, and 0.76, respectively in RF model. The results of the calibration curves indicated that the calibration curves of the RF models closely resembled the ideal curves. The DCA curves exhibited a net clinical benefit in comparison to treatment for or treatment for none for all models, with the exception of CNB. Conclusions The machine learning algorithm utilized in this study effectively generated a prediction model for delays in seeking medical care for patients with breast cancer. The best RF model's remarkable predictive power, exhibiting a good discrimination and calibration.