Open AccessMumps prediction based on improved support vector machine
Author(s) -
Yongna Li,
Huacan Song,
Jiulei Jiang,
Shaoping Jiang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012101
Subject(s) - support vector machine , polynomial kernel , kernel (algebra) , radial basis function kernel , machine learning , computer science , artificial intelligence , kernel method , function (biology) , least squares support vector machine , data mining , mathematics , combinatorics , evolutionary biology , biology
The prevention of d isease, has the important guiding significance to safeguard the health of the people. In order to improve the accuracy rate of mumps disease forecast of Yinchuan City, Ning xia, joined the combined kernel function idea based on regression prediction model of the traditional Support Vector Machine, the polynomial and radial basis kernel function combination instead of the traditional single kernel function model, proposed a combination of kernel function of support vector machine algorithm, with mumps Ning xia disease for the practice of Yinchuan City, to conduct research and forecast for the pathogenesis of rules combined with the local weather changes. The meteorological data and the number of diseases using correlation analysis method for processing, select the factor related coefficient ordering large from, then these factors should be selected using a single kernel and mixed kernel function for processing, training and prediction using support vector machine. The experimental results show that the mixed kernel function of Support vector machine prediction model results than the single kernel function regression is better