Medical data modeling-based class prediction method of necrotizing enterocolitis (NEC)

Abstract

The invention provides a medical data modeling-based class prediction method of necrotizing enterocolitis(NEC). The method includes the following steps: data sample selection, feature screening, important feature selection, model establishment and prediction result scoring. According to the method, systematic analysis and modeling are carried out on relevant data of the necrotizing enterocolitis, an evaluation method of model prediction is given, through a model, effective auxiliary diagnosis can be carried out on the necrotizing enterocolitis of patients based on the data of the necrotizing enterocolitis, effective prevention, intervention and treatment are enabled to be carried out at an initial disease period, and a basis is provided for achieving the best treatment effect.

Description

technical field [0001] The invention relates to the technical field of medical prediction, in particular to a method for predicting necrotizing enterocolitis based on medical data modeling. Background technique [0002] Necrotizing enterocolitis (Necrotizing Enterocolitis, NEC) is a common disease that threatens low birth weight newborns in the first week of life. A frequency of 1% to 5% has been reported in neonatal intensive care. Data show that 7% of newborns with a birth weight of less than 1 kg have definitive necrotizing enterocolitis. There are two types of necrotizing enterocolitis: medical necrotizing enterocolitis (Non-progressive NEC) and surgical necrotizing enterocolitis (Surgical NEC). Called progressive necrotizing enterocolitis (Progressive NEC). The overall mortality rate of patients with necrotizing enterocolitis is 15% to 30%, and the mortality rate is higher in progressive necrotizing enterocolitis. [0003] At present, the diagnosis of medical necrot...

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Problems solved by technology

[0004] However, the above method uses a model that is too simple to effectively discover important feature sets and improve model performance and accuracy.

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