A Hierarchical Important Feature Selection Method Based on Clinical High-Dimensional Data of Breast Cancer

Abstract

The invention discloses a hierarchical important feature selection method based on clinical high-dimensional data of breast cancer. The feature selection method of the present invention includes statistical feature selection and integrated feature selection, wherein the statistical feature selection adopts the single factor analysis method, and the features that have a significant impact on the outcome variable are initially selected through different statistical tests; the integrated feature selection is achieved by establishing a gradient boosting tree After the model is trained, the feature importance score is obtained, and then the designed and verified importance score threshold is used to realize the feature selection that has an important impact on the outcome variable. The invention can effectively overcome the problems of excessively high data feature dimension, too many redundant features, messy data and the like in the process of clinical breast cancer prediction and modeling. Redundant or meaningless features in the high-dimensional data of clinical breast cancer can be excluded, so as to select as few features as possible that have an important impact on breast cancer modeling to ensure the accuracy and practicability of the breast cancer model.

Description

technical field [0001] The invention relates to the fields of computer technology, statistical machine learning technology, feature engineering technology and the like. Background technique [0002] Breast cancer is the malignant tumor with the highest incidence rate among women in the world, which seriously threatens women's health. Breast cancer patients are usually intervened through surgery, chemotherapy and other treatment measures, and may face the risk of recurrence at any time after treatment. Scientifically assessing and predicting the survival status of breast cancer patients can assist doctors in formulating appropriate treatment plans, providing new support for reducing the risk of recurrence and improving prognosis. [0003] To realize the assessment and prediction of the survival status of breast cancer patients, such as the recurrence-free survival rate, a machine learning prediction model can be established based on breast cancer clinical data. However, cli...

AI Technical Summary

Problems solved by technology

[0013] The purpose of the present invention is to solve the problem of too high clinical data dimension in establishing breast cancer survival prediction model

Use the hierarchical feature selection method combining statistical feature selection and integrated feature selection to solve the problem of important feature extraction and model practicability

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