Human physiological feature selection algorithm based on combination of filtering and improved clustering

Abstract

The invention discloses a human physiological feature selection algorithm based on combination of filtering and improved clustering. The algorithm includes S1, selecting an impedance model, collectingfirst feature parameters and second feature parameters to construct an initial feature set and a final optimal sub set; S2, introducing a filtering algorithm and applying the algorithm on each feature in the collected data; S3, ranking the feature set according to HSIC values from large to small; S4, adding features ranked first K into the feature set and filtering away body component unrelated parameters by utilizing the filtering algorithm, and constructing an initial data set; S5, constructing a feature sparse graph based on the data set according to the clustering algorithm; S6, utilizingthe improved clustering algorithm to screen redundancy features in the clusters. By adopting the algorithm provided by the invention, human body component predication precision can be improved, and amore effective detection means can be provided for human body component study and clinic application.

Description

technical field [0001] The invention belongs to the field of bioinformatics, in particular to a human physiological feature selection algorithm based on the combination of filtering and improved clustering. Background technique [0002] The balance of body composition plays an important role in maintaining the stability of the internal environment of the body, and is an important factor affecting human health. When disease occurs, changes in body composition often precede clinical symptoms of the disease. Therefore, changes in body composition can be used to predict the correlation of diseases such as hypertension, dyslipidemia, and metabolic syndrome. However, there are many relevant parameters that affect human body composition, and there are characteristics such as high nonlinearity, redundancy, and irrelevance among the parameters. [0003] The existing Wrapper algorithm removes redundant features. This method can obtain better general performance, but due to the high ...

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Problems solved by technology

However, there are many relevant parameters that affect human body composition, and there are characteristics such as high nonlinearity, redundancy, and irrelevance among the parameters.

[0003] The existing Wrapper algorithm removes redundant features. This method can obtain better general performance, but due to the high complexity of the algorithm, it is not suitable for large-scale data sets; the Filter algorithm assigns a weight value to each feature according to the calculation results of the criteria, and calculates The efficiency is high, but this method does not fully consider the redundancy between features, and the selected feature subset is likely to have a large amount of redundancy; the clustering method divides the body component parameter data into multiple groups or clusters, making the cluster The inner objects have a high similarity. According to the distance between each cluster and the center point, the judgment is made to effectively screen out redundant features, but it cannot effectively screen out irrelevant features.

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