This invention discloses an intelligent recommendation and optimization method for
refractive surgery types based on corneal
biomechanics, relating to the fields of intelligent medical decision-making and ophthalmic
refractive surgery planning. After collecting preoperative feature sets of patients, this invention outputs a range of potential
surgical procedures through a preliminary screening model that integrates a
rule engine and LightGBM. By dividing the historical feature sets of four
surgical procedures—ICL, SMILE,
LASIK, and SURFACE—a two-
stage classification and prediction framework is constructed. First, it distinguishes between ICL and corneal
refractive surgery, and then further subdivides the corneal refractive
surgery types. During training, optimization models are employed using five-fold cross-validation and SMOTE to output recommended
surgical procedures. An
interpretability force graph is drawn based on SHAP values to improve the accuracy and safety of surgical procedure recommendations. This invention is the first to systematically incorporate corneal biomechanical parameters into a refractive
surgery prediction model, achieving intelligent recommendation within a unified framework for multiple surgical procedures. It provides a reliable auxiliary decision-making basis for refractive
surgery planning and has broad application value.