多重线性回归与机器学习在预测初诊乳腺癌患者癌症复发恐惧的比较

方超; 李洋; 熊梅棣; 刘茜茜; 程玉萍; 张春华

doi:10.14188/j.1671-8852.2023.0670

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多重线性回归与机器学习在预测初诊乳腺癌患者癌症复发恐惧的比较

临床医学研究 | 更新时间：2026-11-05

- 多重线性回归与机器学习在预测初诊乳腺癌患者癌症复发恐惧的比较
- Comparison of multiple linear regression and machine learning in predicting fear of cancer recurrence in newly diagnosed breast cancer patients
- 武汉大学学报（医学版） 2025年46卷第7期页码：883-889
- 作者机构：
  
  1.武汉大学中南医院，麻醉科，湖北武汉 430071
  2.武汉大学中南医院，内分泌科，湖北武汉 430071
  3.武汉大学中南医院，护理部，湖北武汉 430071
  4.武汉大学中南医院，甲乳外科，湖北武汉 430071
- 作者简介：
  
  方超，男，1989—，护理硕士生，主要从事肿瘤康复相关研究，E⁃mail:fangchao926@whu.edu.cn
  张春华，女，1973—，医学硕士，主任护师，主要从事肿瘤相关护理、护理管理的研究，E⁃mail:zhangchua@whu.edu.cn
- 基金信息：
  
  武汉大学中南医院护理学科培育项目(ZNXKPY2022006);武汉大学临床护理学专项科研培养基金(LCHL202304)
- DOI：10.14188/j.1671-8852.2023.0670
  中图分类号： R743.33
- 收稿：2023-12-11，
  
  纸质出版：2025-07-25
- 稿件说明：
移动端阅览
方超,李洋,熊梅棣等.多重线性回归与机器学习在预测初诊乳腺癌患者癌症复发恐惧的比较[J].武汉大学学报（医学版）,2025,46(07):883-889.

FANG Chao,LI Yang,XIONG Meidi,et al.Comparison of multiple linear regression and machine learning in predicting fear of cancer recurrence in newly diagnosed breast cancer patients[J].Medical Journal of Wuhan University,2025,46(07):883-889.
方超,李洋,熊梅棣等.多重线性回归与机器学习在预测初诊乳腺癌患者癌症复发恐惧的比较[J].武汉大学学报（医学版）,2025,46(07):883-889. DOI： 10.14188/j.1671-8852.2023.0670.

FANG Chao,LI Yang,XIONG Meidi,et al.Comparison of multiple linear regression and machine learning in predicting fear of cancer recurrence in newly diagnosed breast cancer patients[J].Medical Journal of Wuhan University,2025,46(07):883-889. DOI： 10.14188/j.1671-8852.2023.0670.

摘要

目的

采用轻量级梯度提升机(LightGBM)、极端梯度提升(XGBoost)、决策树(DT)、随机森林(RF)4种机器学习模型与多元线性模型（MLR）分析初诊乳腺癌患者癌症复发恐惧的影响因素。

方法

采用一般资料调查表、恐惧疾病进展简化量表、社会支持评定量表及医学应对问卷对339例乳腺癌患者进行调查。采用单因素及相关性分析筛选危险因素，基于XGBoost、LightGBM、DT、RF算法及MLR分别建立预测模型，使用平均绝对误差(MAE)、均方根误差(RMSE)及决定系数(

²)等指标比较预测性能。

结果

基于4种机器学习与MLR预测模型显示患者职业、工作状态、家庭人均月收入、社会支持水平及医学应对方式的面对和回避是初诊乳腺癌患者癌症复发恐惧的主要影响因素。

结论

基于机器学习算法尤其是LightGBM建立的风险预测模型具有较好的效能，医务人员可以根据预测模型结果制定相应的干预措施，以降低患者癌症复发恐惧水平，从而提高患者生活质量。

Abstract

Objective

To analyze the factors affecting the fear of cancer recurrence in breast cancer patients using four machine learning models and multiple linear regression: light gradient boosting machine (LightGBM)

extreme gradient boosting (XGBoost)

decision tree (DT)

and random forest (RF)

and compare them with multiple linear regression (MLR).

Methods

The 339 newly diagnosed breast cancer patients were investigated by the general data questionnaire

fear of progression questionnaire⁃short form (FoP⁃Q⁃SF)

social support rating scale (SSRS)

and medical coping modes questionnaire (MCMQ). Single⁃factor and correlation analyses were used to screen risk factors. Predictive models were built b

ased on XGBoost

LightGBM

RF algorithms

and MLR analysis. Their predictive performance was compared using mean absolute error (MAE)

root mean squared error (RMSE)

and coefficient of determination (also known as

²).

Results

Based on four machine learning models and MLR predictions

occupation

employment status

monthly per capita household income

level of social support

and coping strategies (facing and avoiding) were the primary factors influencing fear of cancer recurrence in newly diagnosed breast cancer patients.

Conclusion

The risk prediction model built based on machine learning algorithms

especially LightGBM

has good performance. Healthcare professionals can formulate corresponding intervention measures based on the results of the prediction model to reduce patients' fear of cancer recurrence

thereby improving their quality of life.

关键词

Keywords

references

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