Kawasaki disease classification and prediction method based on medical data modeling

Abstract

The invention provides a Kawasaki disease classification and prediction method based on medical data modeling. The method comprises the following steps: S1, data sample selection: extracting a valid sample which can be modeled from a sample data set; S2, feature sieving: sieving 19 features conforming to field medical auxiliary diagnosis application from a feature set for constructing sample data for modeling; S3, Kawasaki disease classifying model construction and evaluation: fitting an Xtrain data set on a training set by using a random forest classifying method, recording optimal modeling parameters and weights of all selected features, and classifying and predicting a test set sample according to the classifying model. In the Kawasaki disease classification and prediction method, Kawasaki disease relevant data are analyzed and modelled systemically, and an evaluation method for model prediction is provided, so that the model can be used for performing effective auxiliary diagnosis on a Kawasaki disease of a patient based on Kawasaki disease data, effective prevention and intervention and treatment are performed at the early stage of attacking, and a basis is laid for a best treatment effect.

Description

technical field [0001] The invention relates to the technical field of medical prediction, in particular to a Kawasaki disease classification prediction method based on medical data modeling. Background technique [0002] Kawasaki disease (KD) is an acute, self-limiting acute inflammatory vasculitis of unknown etiology, and has become the most common acquired heart disease in infants and young children. Failure to promptly diagnose and treat infants with Kawasaki disease with intravenous immune globulin (IVIG) can lead to dilated coronary arteries or aneurysms. The pathogenesis of Kawasaki disease is currently unknown, there is no effective diagnostic test, and it can easily be misdiagnosed as common fever. In addition, misdiagnosis of children with Kawasaki disease with cardiovascular sequelae may lead to myocardial infarction and death in 25% of cases. [0003] The Kawasaki disease classification prediction model based on medical data modeling can assist diagnosis, help ...

AI Technical Summary

Problems solved by technology

[0004] The model constructed by the linear method is simple, and the results are easy to be understood by doctors, but it cannot effectively use the nonlinear factors of the characteristics of the data samples to improve the performance and accuracy of the model

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