Hierarchical important feature selection method based on clinical high-dimensional breast cancer data
A high-dimensional data and breast cancer technology, applied in the computer field, can solve the problems of high-dimensional clinical data, achieve the effects of ensuring accuracy, reducing time and computing resource consumption, and widely applicable scenarios
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[0040] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the implementation methods and accompanying drawings.
[0041] see figure 1 , The layered important feature selection method for breast cancer clinical high-dimensional data of the present invention includes statistical feature calculation, integrated feature calculation and threshold value setting methods involved in integrated feature calculation. The present invention uses a layered feature selection method combining statistical feature selection and integrated feature selection to effectively solve the problems of important feature extraction and model practicability. Its specific implementation process is as follows:
[0042] S1: Statistical feature selection.
[0043] Feature extraction and cleaning are performed on the original clinical data to obtain the original feature set F n; ...
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