The invention discloses an acute necrotizing
pancreatitis severe prediction model construction method based on a
machine learning method. The acute necrotizing
pancreatitis severe prediction model construction method comprises the following steps: S1, collecting
patient data based on an inclusion standard and an exclusion standard; s2, data preprocessing and
feature selection; s3, obtaining an optimal
differential diagnosis prediction model in combination with
radiomics characteristics and a plurality of
machine learning algorithms; s4, obtaining an evaluation index through prediction
performance comparison; and S5, constructing a
pancreas model, a
pancreas surrounding model and a combination model, and producing an ROC curve interpretation result. The present invention develops and verifies a
machine learning model for distinguishing between severe and moderate ANPs (i.e., ANSP and ANMSP). These models will be based on
radiomics features extracted from pancreatic
parenchyma portal vein phase CECT images, peripancreatic necrotic lesions, and combinations thereof. By evaluating the diagnostic performance of the models, early recognition of
disease severity is realized, and support is provided for clinical decisions related to treatment.