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Patient physiological data feature selection method based on distance greedy strategy

A feature selection method and physiological data technology, applied in the medical field, can solve the problems of low search efficiency, slow convergence speed, easy to fall into local optimal solutions, etc., to improve classification accuracy, improve convergence speed, and reduce data feature redundancy. Effect

Active Publication Date: 2019-03-29
HUBEI UNIV OF TECH
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Problems solved by technology

[0007] The purpose of the present invention is to solve the problems of slow convergence speed, low search efficiency and easy to fall into the local optimal solution in the current patient physiological data feature selection algorithm, and proposes a gray wolf feature selection method based on the distance greedy strategy, which improves the The accuracy of algorithm classification reduces data feature redundancy

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  • Patient physiological data feature selection method based on distance greedy strategy
  • Patient physiological data feature selection method based on distance greedy strategy
  • Patient physiological data feature selection method based on distance greedy strategy

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Embodiment Construction

[0024] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0025] The core of the technology of the present invention is to regard the feature selection problem of medical data containing N features as a discrete optimal combination problem in binary N-dimensional space, and each feature subset can be represented by an N-dimensional binary vector. The gray wolf optimization algorithm searches in N-dimensional binary space.

[0026] please see figure 1 , a kind of patient physiological data characteristic selection method based on distance greedy strategy provided by the present invention, comprises the following ste...

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Abstract

The invention discloses a patient physiological data feature selection method based on a distance greedy strategy. Aiming at the disadvantage of a conventional feature selection algorithm in performance, the invention achieves the improvement, and the algorithm employs the greedy strategy for improving a position updating part in an original grey wolf algorithm, thereby improving the capability ofthe algorithm in developing the optimal solution, and improving the convergence rate. Moreover, the algorithm can effectively improve the classification accuracy, and reduces the data feature redundancy.

Description

technical field [0001] The invention belongs to the field of medical technology, and relates to a feature selection method of patient physiological data, in particular to a gray wolf feature selection method based on a distance-greedy strategy. Background technique [0002] Today, with the rapid development of science and technology, the medical testing system is constantly being updated, and the testing system is becoming more and more mature. As a killer of human health, heart disease is of great significance to detect it before the onset of the disease. However, the patient's physiological data features are large and complex, and the complex features make the workload of heart disease detection huge, and the effect will become poor. Gray wolf optimization (GWO) is a swarm intelligence algorithm that is now being used. This algorithm determines the position of the prey to be prey by simulating the process of wolves preying on prey, which is the optimal solution to the opt...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G06N3/00G06N3/12
CPCG06N3/006G06N3/126G16H50/20
Inventor 钮焱李军童坤刘宇强李星
Owner HUBEI UNIV OF TECH
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