Clinic pathology data classification method based on combination of principal component analysis and extreme learning machine

Abstract

The present invention relates to a clinic pathology data classification method based on combination of principal component analysis and an extreme learning machine. The classification method comprises the following steps of (1) normalizing clinic data, extracting features through principal component analysis, ordering characteristic values according to characteristic significance, removing data dimensions below a significance threshold, and achieving the purpose of data dimension reduction; (2) training a feedforward neural network classifier by using an extreme learning algorithm after data dimension reduction is carried out; and (3) using the trained feedforward neural network classifier to test test samples, and obtaining a classification result. According to the clinic pathology data classification method based on combination of the principal component analysis and the extreme learning machine, dimensions are effectively reduced, calculation is simplified, and the classification effect is good.

Description

technical field [0001] The invention relates to a data classification technology, in particular to a pathological data classification method based on the combination of principal component analysis and extreme learning machine, which can be effectively applied to the classification and mining of high-dimensional clinical pathological data. Background technique [0002] Clinical physiological indicators are the basic basis for medical practice such as medical diagnosis, treatment, and prognosis. The dimensions of clinical data are very high. When doctors make a diagnosis, they often use their professional medical knowledge and diagnostic experience to make speculations. This diagnostic experience is a comprehensive ability of intuitive knowledge and acquired training, which is accumulated by doctors in years of experience in clinical diagnosis. However, the internal mechanism of the disease is intricate, and various factors will affect each other. The relationship between it...

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Problems solved by technology

[0004] In order to overcome the shortcomings of the existing medical information data mining methods, such as high dimensionality, complex calculation, and poor classification effect, the present invention...

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