Prediction model based on optimized extreme learning machine (ELM)

Abstract

The invention discloses a cardiovascular disease (CVD) prediction model using an extreme learning machine (ELM) algorithm based on single-hidden layer feed-forward neural networks (SLFNs), which means an ELPSO-ELM hybrid intelligent algorithm prediction model. The ELPSO-ELM hybrid intelligent algorithm prediction model is designed by using the algorithm through the following steps: building a leadership particle enhanced particle swarm optimization algorithm (ELPSO) by carrying out five stages of continuous variation modes, which are respectively the Gaussian variation, Cauchy variation, Pg each dimension opposition-based variation, Pg overall opposition-based variation and DE-based variation; and optimizing the hidden layer unit parameters of the SLFNs by using the optimization strategy of the particle swarm optimization (PSO) algorithm. According to the ELPSO-ELM hybrid intelligent algorithm prediction model disclosed by the invention, the limitations of traditional models which contain less prediction factors and have high data requirements can be solved.

Description

technical field [0001] The invention relates to the field of prediction models, in particular to a prediction model based on an optimized extreme learning machine. Background technique [0002] At present, classic Framingham, SCORE, WHO and other models have achieved good prediction results in 10-year incidence risk prediction. The main modeling ideas include: the inference prediction method based on the trending reasoning based on the epidemic process and characteristics of the disease, and the qualitative prediction based on the knowledge level and subjective experience of experts on cardiovascular disease (CVD); large follow-up cohorts based on disease risk factors and incidence Mathematical forecasting methods for cross-sectional data, such as time series forecasting models and regression forecasting models, establish mathematical probability models for regularly changing data and quantitatively mine the proportional relationship between pathogenic factors, which require...

AI Technical Summary

Problems solved by technology

[0003] Under the current conditions of complicated pathogenic factors, the above regression analysis strategy cannot solve the problem of multi-classification factor fitting.

In addition, probabilistic strategies have high requirements for independent variable data, and it is difficult to apply them in the absence of large-scale cross-sectional research data

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