Breast cancer prediction method based on penalty COX regression

A prediction method, breast cancer technology, applied in medical data mining, machine learning, instruments, etc., to avoid instability, improve prediction performance, and reduce variance

Pending Publication Date: 2022-03-04
SHANDONG UNIV
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  • Claims
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Problems solved by technology

[0006] Aiming at the deficiencies of the prior art, the present invention provides a breast cancer prediction method based on penalized COX regression, which solves a series of disadvantages of the tradition

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  • Breast cancer prediction method based on penalty COX regression
  • Breast cancer prediction method based on penalty COX regression
  • Breast cancer prediction method based on penalty COX regression

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Embodiment Construction

[0039]The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0040] see Figure 1-3 : A breast cancer prediction method based on penalized COX regression, including the following steps:

[0041] S1: Questionnaire design:

[0042] A questionnaire was designed based on the epidemiological characteristics of breast cancer and related influencing factors. The questionnaire involved non-experimental risk factors in multiple dimensions such as genetic factors, high-fat diet, lack of exercise, sleep, and psychology.

[0043] S2: Follow-up dat...

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Abstract

The invention discloses a breast cancer prediction method based on penalty COX regression, which comprises the following steps of: processing follow-up data into survival data for later use, taking all prediction factors after data preprocessing as input variables of a model, sampling through a bootstrap method to obtain T self-service sample sets, a penalty COX regression model is independently constructed on the basis of different self-service sample sets to serve as a base predictor of integrated learning, after the base predictors are constructed, a simple average method is used for combining the T base predictors, and finally an integrated penalty COX regression model is formed to serve as an integrated predictor for breast cancer incidence prediction. According to the breast cancer prediction method based on penalty COX regression, a unique structure of a Bagging integrated framework and a penalty regression model is adopted, and the relationship between different dimension factors and female breast cancer onset risks in China is favorably discussed, so that doctors are assisted to give suggestions for preventing breast cancer onset, the variance of an estimator can be reduced, and the prediction accuracy is improved. The instability of estimation of a single classifier is avoided, and the prediction performance is improved.

Description

technical field [0001] The invention relates to the technical field of breast cancer risk prediction, in particular to a breast cancer prediction method based on penalized COX regression. Background technique [0002] Breast cancer is one of the most common malignant tumors in women all over the world, and its incidence is increasing year by year. The situation of prevention and control is severe, which seriously threatens the lives and health of women. In 2020, there will be about 416,000 new cases of breast cancer in my country, and the growth rate of the incidence rate exceeds the global average. Although with the improvement of medical level, breast cancer has become one of the solid tumors with the best curative effect, but the low early diagnosis rate of breast cancer patients in my country makes the survival time of breast cancer patients in China far lower than that in European and American countries. Therefore, early detection and early treatment are the key to red...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/70G06N20/00
CPCG16H50/20G16H50/70G06N20/00
Inventor 余之刚陈增敬何勇刘丽媛考春雨王斐杨芙范叶叶
Owner SHANDONG UNIV
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