Developing an interpretable machine learning model for easily detecting insulin resistance among breast cancer survivors: a cross-sectional study

This study demonstrates the feasibility of using machine learning algorithms to accurately predict insulin resistance in individuals who have survived breast cancer with a limited set of clinical and demographic variables. The Random Forest and Support Vector Machine models, in particular, offer strong classification performance and may support clinicians in early identification and management of insulin resistance among individuals in this high-risk population.