Random forest classification method used for coronary heart disease data classification and based on kernel extreme learning machine and parallelization

Abstract

The invention discloses a random forest classification method used for coronary heart disease data classification and based on a kernel extreme learning machine and parallelization. Sampling with replacement is performed on a coronary heart disease sample set by using a Bootstrap method so that different coronary heart disease data training subsets and test subsets are generated to be used for base classifiers; the kernel function of the hybrid kernel form is used as the kernel function of the kernel extreme learning machine so that the influence of the kernel type on the performance of the classification model can be reduced; model training is performed on the kernel extreme learning machine by using the coronary heart disease data training subsets and performing testing is performed on the base classifiers by using the test subsets, cyclic judgment is performed by using the mode of sorting and particle swarm optimization and the optimized new base classifiers are regenerated and thebase classifiers having poor classification performance are eliminated and substituted so that the objective of enhancing the overall classification performance can be achieved; and the random forestmodel is formed and then the classification result is selected by using a relative majority voting method.

Description

technical field [0001] The invention belongs to the field of computer software, and in particular relates to a random forest classification method based on kernel extreme learning machine and parallelization for coronary heart disease data classification. Background technique [0002] Medical data show that coronary heart disease has become one of the most serious diseases that endanger human health. One of the characteristics of coronary heart disease is that it is difficult to make an accurate diagnosis in advance, but there are certain rules to follow in the law of its occurrence and development. In machine learning technology, the diagnosis of coronary heart disease is essentially a classification problem. With the development and application of machine learning technology in the medical field, people hope to use machine learning technology and methods to assist in the diagnosis of complex diseases such as coronary heart disease and avoid Doctors can get more accurate d...

AI Technical Summary

Problems solved by technology

Traditionally, a single-machine multi-thread method is used to parallelize the program. However, for massive data, the single-machine multi-thread method is still not feasible, and multiple machines need to be used for parallelization.

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