The present application relates to the technical field of
pregnancy rate prediction, and particularly relates to a model establishment method and
system for predicting
pregnancy rate of early-onset ovarian dysfunction, which obtains clinical data of a patient, identifies four types of prediction key points, including basic physiological characteristics,
ovarian reserve function,
treatment intervention and
pregnancy outcome correlation. When the total amount of key points exceeds a first threshold value, a
dimensionality reduction improved
feedforward neural network is constructed, and basic physiological characteristics and
treatment intervention data are used as inputs to predict
ovarian function and pregnancy outcome. When the number of key points is small, a graph neural network is used, a
directed graph structure is constructed based on clustering and hierarchical relationship of each key point, and node state is dynamically updated based on basic physiological characteristics, so as to realize classification prediction of
ovarian function and pregnancy risk, improve prediction accuracy and model
interpretability, and provide personalized and
intelligent decision support for
fertility evaluation of POI patients.