Medical disease feature selection method based on improved salp swarm algorithm

Patent metadata

Field	Value
Publication number	US-2023029947-A1
Application number	US-202217860077-A
Country	US
Kind code	A1
Filing date	Jul 7, 2022
Priority date	Jul 23, 2021
Publication date	Feb 2, 2023
Grant date	—

Abstract

The disclosure is a medical disease feature selection method based on an improved salp swarm algorithm. The improved salp swarm algorithm is used to optimize a feature selection problem, the accuracy of the mentioned method is estimated by means of a transfer function and classification by a K-nearest neighbor algorithm, and the salp swarm algorithm is improved by using a self-adapted control parameter and an elite grey wolf ruling policy, so that it helps the algorithm avoid premature convergence in the optimization process and jump out of local optimum, thereby achieving a target of the algorithm with the smallest selected feature quantity and the highest classification precision. The method has the advantages of high rate of convergence, higher classification precision and better robustness.

First claim

What is claimed is:

1 . A medical disease feature selection method based on an improved salp swarm algorithm, the method comprises the following steps:
Step S1, acquiring a microarray gene data set of medical diseases, marking a line number of the microarray gene data set of the medical diseases as m and a column number thereof as n, wherein the microarray gene data set of the medical diseases, which is obtained, is formed by arranging m*n gene feature data according to m lines and n columns; partitioning the microarray gene data set of the medical diseases into 10 subsets randomly by using a 10-cross validation function, wherein a line number of each of the 10 subsets is greater than or equal to 1 and column numbers are n; and selecting one subset from the 10 subsets as a validation set, the rest of the subsets being training sets; Step S2, defining a female salp population Y, wherein a size of the female salp population Y being M=20, i.e., there are M individuals in the female salp population Y, and each of the M individuals in the female salp population Yis respectively represented by a data matrix formed by arranging n dimensionality values in one line and n columns; and then performing initialized assignment to each of the dimensionality values of each individual in the female salp population Y by using a random number between 0 and 1 to obtain 0th of the female salp population Y°; Step S3, setting a global optimum fitness value is best, performing initialized assignment of best to positive infinity, setting a global optimal individual to be a bestposition, and initially setting the bestposition as a data matrix [0, 0, 0, . . . , 0] with one line and n columns; Step S4, setting a maximum time of an iteration of the female salp population T=50, setting an iterative time variable t and initially setting t as 1; Step S5, performing a t th iteration on the female salp population, an iteration process specifically including: Step S5.1, converting each dimensionality value of each individual in a (t−1) th generation female salp population Y t−1 into 0 or 1 via transfer functions shown in formulae (1)-(2) to obtain a t th generation binary salp population Bt;

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wherein Y i,j t−1 represents a dimensionality value in the j th column of the i th individual in the (t−1) th generation female salp population, i is equal to 1, 2, 3, . . . M, j is equal to 1, 2, 3, . . . n, B i,j t represents a dimensionality value in the j th column of the i th individual in the t th generation binary salp population, r is a random number between 0 and 1 and is generated a random function before operation every time, and e is a natural constant;
Step S5.2, constructing feature subsets of each individual in the (t−1) th generation female salp population, the step S5.2 specifically including: judging whether the dimensionality value of eac

Assignees

• Univ Wenzhou

Inventors

• Wang Pengjun
• Zhao Songwei
• Chen Huiling
• Xu Suling
• He Wenming
• Shi Yijian

Classifications

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H50/70 Primary

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H10/40

  for data related to laboratory analysis, e.g. patient specimen analysis · CPC title

• G06N3/126 Primary

  Evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title